Method and system for self-aggregation of personal data and control thereof

ABSTRACT

A method includes receiving, by a logic layer processor, over a communication network, from a plurality of electronic resources, initial user personal identifiable information (PII) of a user of a plurality of users. The user PII includes a plurality of data elements. The plurality of data elements of the initial PII of the user are classified to populate a profile map data structure having a standardized predefined data schema of a plurality of vector elements so as to form a user-specific profile map data structure of the user. Additional user personal identifiable information (PII) of the user is iteratively received from the plurality of electronic resources. The additional user PII of the user is iteratively classified to update the user-specific profile map data structure of the user. A plurality of user-specific data management software functions is enabled based on the user-specific profile map data structure.

FIELD OF TECHNOLOGY

The disclosure generally relates to personal information management and,more particularly, to a method and system for self-aggregation ofpersonal data and control thereof.

BACKGROUND OF TECHNOLOGY

Today, many products and services from different entities may needdetailed user data known as Personal Identifiable Information (PII) thatneeds to be verifiable (or verified). These entities may includeGovernment agencies, NGOs, providers of financial related professionalservices (CPA to CFPs) service, credit card companies, as well as otherconsumer lenders and especially home mortgages, borrowers' securedconsumer lenders from car loans and leasing, Home Equity lines ofcredits, and the largest of all markets—consumer Mortgage lenders.

Two trends have created a fundamental conflict for both the user and theproviders of the products and services mentioned above. The first trendis the evolution of laws relating to the rights that a user has to theirPII and regulatory compliance requirements. New legal and regulatoryframeworks have created a fundamental change in how PII needs to betreated and who has rights to it. Both on a Federal and State level,laws are on the books or are in legislation. The EU Law General DataProtection Act also known as GDPR has accelerated this evolution ofconsumer rights both prompting new legislation in other countries andjurisdiction (e.g. California Consumer Protection Act), as well as highconsumer awareness of how their data is used and the rights they haveover their PII data used by companies. The second trend is userperceptions regarding how their PII is being used.

Users across all demographics may increasingly use digital means as away to shop for services and/or products. Users may have an expectationof ease of use and convenience in every product or service they areoffered. In addition, users want personalized and/or customized offersthat address their particular needs, preferences, etc. This may affectmany different companies in various industries and the methods thesecompanies can use to offer their products or services. Two of theprimary users of PII data are the Banking and Financial ServicesIndustry. One of the fastest growing sectors is financial technologysector, known as Fintech.

These companies may use the user's PII of a particular user to determinewhat products that a company may be able to provide and if the companymay provide them to user based on evaluating the user's PII. Of theindustries that use the user's PII, the home mortgage industry may havethe most acute challenges. This is due to the extensive set of the PIIdata required as well as assuring the accuracy and validity of the PIIdata, and the fact that it affects so many users (either who have orwant to have a home with a mortgage). The financial sums from newapplications may exceed a trillion dollars every year.

The conflicting factors mentioned above may be evident in the mortgageprocess. For example, a user (borrower) may need to fill out a loanapplication information. The loan application data may use informationbased on the dataset from the Government Sponsored Entity Form commonlyknown as the FNMA 1003, for example. The user may need to providesupporting documentation to substantiate, or to validate thisinformation. This is often an incremental serial process that is timeintensive for both the consumer and the lender.

Historically, verification was done using a series of directverification forms for each data point, e.g. Verification of Mortgage(VOM), Verification of Employment (VOE) (e.g., income), and Verificationof Deposit (VOD), for example. The industry adopted two trends forspeeding up the process (at the time it was over 60 days fromapplication to funding a mortgage), partially substituting thoseverification forms with electronically provided data from trustedparties.

Before going through the validation process, a common practice is to“pre-qualify”, which may be indicative of the statistical probabilitythat the lender may provide the loan to the user. However, given the newPII regulatory landscape, even with the prequalification data, thelender may bear liability if the user's PII is not handled in aregulatory compliant secure and private manner.

Once the lender prequalifies the user, and the user wants to obtain theloan offered, the lender may initiate loan processing and loanunderwriting. These stages may be the most time intensive for both theborrower (user) and the lender. The primary objective is to collect acomplete set of the user's data, validate the user's data, and thenanalyze the user's data. The lender may assess whether the loan may beoffered to the user after the dataset of the user's data is complete,verified, and analyzed. This whole process may be expensive and may cost$7000 on the average (in 2018), for example, for a home mortgage. Theacquisition of the required user information as well as the methods forvalidating the user's information has been standardized by theGovernment Sponsored Entities (GSE) commonly known as Fannie Mae (FNMAFederal National Mortgage Association) and Freddy Mac (FNMC FederalNational Mortgage Corporation) over the past 30 years. In recent years,new initiatives may broadly standardize the data formats such as forexample, the Mortgage Industry Standards Maintenance Organization(MISMO®), and the Industry Loan Application Dataset (iLAD), which is asupplemental specification to the Fannie Mae®/Freddie Mac®specification—Uniform Loan Application Dataset (ULAD). These initiativesare being done since the data collection and verification process are soproblematic.

The data collection and verification problem may be more difficult forthe borrower, as they often have to go through a repetitious process ofcollecting data, collaborating documentation and explanations. Thisprocess can be both time-consuming as well as frustrating for theborrower. This is also a significant component of the Lender cost tocreate a loan mentioned above.

Therefore, there may be a need in the financial industry for a procedurefor expediting and facilitating the processing of financial transactionsand loans. Collecting, validation and conveyance of an extensive set ofPII data to the Financial Services and Banking industry sectors, whileallowing the consumer the control and transparency they want and thatcomplies with the regulatory requirements of handling this data for thebenefit of both the consumer and the broad scope of industry sectorsthat need the consumer PII.

There may be a need in the financial industry for a procedure forexpediting and facilitating the collection, validation, and processingof a consumers' PII and sharing it with the Financial Services andBanking industry sectors, while allowing the consumer to maintaincontrol of the data. This provides the transparency consumers need andprovide the Enterprise with an environment and data set that is bothvalidated. This reduces the cost of the Enterprise's compliance with PIIrelated regulations, which benefits both the consumer and Enterprises ina broad scope of industry sectors that use consumer PII.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based method that includes at least thefollowing steps of receiving, by a logic layer processor, over acommunication network, from a plurality of electronic resources, initialuser personal identifiable information (PII) of a user of a plurality ofusers, where user PII may include a plurality of data elements. Theplurality of data elements of the initial PII of the user may beclassified to populate a profile map data structure having astandardized predefined data schema of a plurality of vector elements soas to form a user-specific profile map data structure of the user, thatmay include at least a plurality of: (i) a demographic user-specificparameter, (ii) a psychographic user-specific parameter, (iii) abehavioral user-specific parameter, (iv) a quantitative user-specificparameter, or (v) any combination thereof. Additional user personalidentifiable information (PII) of the user based at least in part on theuser-specific profile map data structure may be iteratively receivedover the communication network, from the plurality of electronicresources. The additional user PII of the user may be iterativelyclassified to update the user-specific profile map data structure of theuser. A plurality of user-specific data management software functionsmay be enabled based on the user-specific profile map data structure.

In some embodiments, the present disclosure provides an exemplarytechnically improved computer-based system that may include anon-transitory memory and at least one logic layer processor. The atleast one logic layer processor may be configured to execute computercode stored in the memory that causes the at least one processor toreceive over a communication network, from a plurality of electronicresources, initial user personal identifiable information (PII) of auser of a plurality of users, where user PII may include a plurality ofdata elements, to classify the plurality of data elements of the initialPII of the user to populate a profile map data structure having astandardized predefined data schema of a plurality of vector elements soas to form a user-specific profile map data structure of the user, thatmay include at least a plurality of: (i) a demographic user-specificparameter, (ii) a psychographic user-specific parameter, (iii) abehavioral user-specific parameter, (iv) a quantitative user-specificparameter, or (v) any combination thereof, to iteratively receive overthe communication network, from the plurality of electronic resources,additional user personal identifiable information (PII) of the userbased at least in part on the user-specific profile map data structure,to iteratively classify the additional user PII of the user to updatethe user-specific profile map data structure of the user, and to enablea plurality of user-specific data management software functions based onthe user-specific profile map data structure.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, wherein like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIG. 1 depicts a system for self-aggregation of personal data of a userand personal data custody, control, and stewardship in accordance withone or more embodiments of the present disclosure;

FIG. 2 depicts a process flow for the self-aggregation of a userpersonal identifiable information of a user in accordance with one ormore embodiments of the present disclosure;

FIGS. 3A and 3B depict a first threshold PII MAP and a second thresholdPII MAP in accordance with one or more embodiments of the presentdisclosure;

FIG. 4 depicts a graphical representation of data types used in thepersonal identifiable information map of a user in accordance with oneor more embodiments of the present disclosure;

FIG. 5 depicts a plurality of data management software functions inaccordance with one or more embodiments of the present disclosure;

FIGS. 6A and 6B depict a first exemplary screenshot and a secondexemplary screenshot of a graphic user interface in accordance with oneor more embodiments of the present disclosure;

FIG. 7 is a flowchart of a method for self-aggregation of personal dataand personal data custody, control, and stewardship in accordance withone or more embodiments of the present disclosure;

FIG. 8 depicts PIIMAP Layers in accordance with one or more embodimentsof the present disclosure;

FIG. 9 depicts application programming interface (API) and Data Schemacompatibilities in accordance with one or more embodiments of thepresent disclosure;

FIG. 10 depicts a third exemplary screenshot of a graphic user interfacein accordance with one or more embodiments of the present disclosure;

FIG. 11 depicts an exemplary flow diagram for a first use case inaccordance with one or more embodiments of the present disclosure;

FIG. 12 depicts a fourth exemplary screenshot of a graphic userinterface in accordance with one or more embodiments of the presentdisclosure;

FIG. 13 depicts an exemplary flow diagram for a second use case inaccordance with one or more embodiments of the present disclosure;

FIG. 14 depicts an exemplary flow diagram for a third use case inaccordance with one or more embodiments of the present disclosure;

FIG. 15 depicts a block diagram of an exemplary computer-basedsystem/platform in accordance with one or more embodiments of thepresent disclosure;

FIG. 16 depicts a block diagram of another exemplary computer-basedsystem/platform in accordance with one or more embodiments of thepresent disclosure; and

FIGS. 17 and 18 are diagrams illustrating implementations of cloudcomputing architecture/aspects with respect to which the disclosedtechnology may be specifically configured to operate, in accordance withone or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

In addition, the term “based on” is not exclusive and allows for beingbased on additional factors not described, unless the context clearlydictates otherwise. In addition, throughout the specification, themeaning of “a,” “an,” and “the” include plural references. The meaningof “in” includes “in” and “on.”

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

As used herein, the term “runtime” corresponds to any behavior that isdynamically determined during an execution of a software application orat least a portion of software application.

As used herein, the acronym PII may refer to Personal IdentifiableInformation. PII is information which may be used to distinguish ortrace an individual's identity, such as their name, social securitynumber, biometric records, etc. alone, or when combined with otherpersonal or identifying information, which may be linked or linkable toa specific individual, such as date and place of birth, mother's maidenname, etc. PII may be data obtained in data aggregation steps usingcredit reports, social media, and user inputs, for example.

As used herein, a PII MAP may be a machine automated presentation of auser's PII which may include data pertaining to the primary values ofthe user's PII and various metadata on the user's PII. This may be astored set of raw data, files, and/or various metadata. This data andtheir sources may include, for example, bank statements, Tax returns,credit card statements, Purchase histories, Medical records, picture ID,etc. The metadata types may include: descriptive metadata, structuralmetadata, administrative metadata, reference metadata, and/orstatistical metadata.

As used herein, PII PRINT may refer to an export of the PII MAP and maybe in a flat file format, image (like a Barcode or QR code), and/or adatabase that is a subset of the PII MAP. The data in the flat file, thedatabase, and/or represented in the Bar Code or QR Code may be raw,anonymized, and/or synthetic. This may provide a method for the analysisof the user's PII data by a third party without the user needing toshare actual personal data or by the third-party taking possession of orcontrolling the data regulated as PII.

As used herein, the terms “Your PIT”, “YP platform”, or “YP”, maysynonymously refer to the platform performing the method forself-aggregation of personal data and for self-aggregation of personaldata of a user and personal data custody, and/or control, and/orstewardship using the PII MAP.

The embodiments disclosed herein may relate to a user's interaction withthe financial services industry, financial institutions, companies inother industry verticals and/or professional service providers (e.g.,that may require the user's PII service and/or related non-governmentalorganizations (NGO) and government offices. Stated differently, this maybe a provider of goods or services that may need an extended set of theconsumer's PII to qualify the user for providing the product or serviceto the user.

The disclosed embodiments may provide methods and systems for expeditingand facilitating the processing, offering, and provisioning of financialtransactions, loan products, and/or services that may require multiplepoints of data considered to be PII to the user, all while allowing theuser to retain better control of their PIT data.

One industry vertical that may require the largest set of PIT data ishome mortgages. Borrowers may need to provide loan applicationinformation and documentation to each lender every time a loan isrequested. Similarly, for each application, lenders may need toincrementally collect and verify data before receiving a determinationif the borrower qualifies for the loan. Financial institutions may needto repeat the process of incremental data collection with validation foreach borrower and for each loan, which is very inefficient. Therefore,there may be a need in the financial industry for a procedure forexpediting and facilitating the processing of financial transactions,interactions, and loans, or managing personal data in any industryvertical. The technical solution as provided in the embodimentsdisclosed herein solves these problems.

Embodiments of the present disclosure herein describe methods andsystems for creating an electronic account for identifying, retrieving,processing, storing and/or providing individually defined access topersonal identifiable information (PII) of a specified user. At leastone secure electronic location may be configured for storage of thespecific user's personal identifiable information and for synchronizingthe secure electronic location with the electronic account to controlthe secure electronic location. The personal identifiable informationmay be aggregated via the electronic account for storage in at least onesecure electronic location, and for processing and accessing via theelectronic account along with details of the manner in which each pointof the personal identifiable information as well as other metadata wasverified including the storage location. The data held within thepersonal identifiable information may be extracting, analyzing,classifying or encrypting data via the electronic account. A datastructure may be generated based on an analysis of the data via theelectronic account where each element in the data structure mayrepresent a unique value. A profile of the specified user may be createdby analysis of the personal identifiable information. Data may beextracted for transactions involving the specified user.

The system may further allow selective access by designated persons tospecified data or objects in at one secure electronic location orspecified elements in the data structure. With the embodiments disclosedherein, users may be in control of their own PII data. Financial serviceproviders may have cost effective, on-demand access to the user's PIIdata, and may access complete user data with validation.

In some embodiments, once a PII MAP is generated for the user from thedata held in at least one secure location, it may be accessed and usedby multiple enterprises based on the permission granted to the consumerenterprises based on the permission granted by the user. Note thatconsumer enterprises may be provider user. The secure electroniclocation may provide a central safe and trusted place to store all ofthe user's personal records (e.g., financial, medical, etc). This mayenable a user to use a single resource to provide their validated PIIdata directly to third parties, such as into a lender's loan originationsystem. For example, commercial users such as Bank of American, Chase,and Capital One, for example, to whom the user has granted permissionfor that instant (transaction) populate their ASCII, file data andvalidation data to via an API, which may be imported directly into theirEllie MAE loan origination system (LOS) software platform. As a result,the lender, for example, may be able to prequalify loan applicants withbetter quality data and less regulatory liability. Furthermore, the usermay be educated with personally tailored educational materialinformation about loans, budgeting, etc.

The result may be a consumer-focused, multi-faceted distributed databaseincluding direct data inputs, as well as images and/or PDF files eitherinputted directly by the consumer or derived from documentation providedby the consumer or from third party verification services authorized bythe user or generated by the user's use of the application. Standarddatasets may be used, such as Industry Loan Application Dataset (iLAD),Uniform Residential Loan Application (URLA), Uniform Loan ApplicationDataset (ULAD), and Mortgage Industry Standards Maintenance Organization(MISMO), for example. By utilizing optical character recognition (OCR)and artificial intelligence (AI), data may be extracted from taxreturns, bank statements, and other financial documents, which may havebeen validated by 3rd parties. Each process date may be marked. The datamay be stored in immutable storage media and/or in blockchain dataset(s)to better ensure accuracy.

Note that from a particular PDF from a particular consumer's Bankstatement file, for example, the artificial intelligence (AI) engine mayhave the context provided by the PIIMAP to interpret the data in thefile and extract it to the PII MAP, as well as to tag the PDF fileitself for storage and retrieval. There may be a number of companies andtechnologies that that may read and extract data from financial PDFdocuments, but there may be limits in the accuracy of AI models evenwhen they are trained due many Natural Language Processing (NLP) issues,for example. However, the YP platform may allow the financial PDFs, forexample, to be interoperated and processed within the context of the PIImap.

The software implemented method may include expediting and facilitatingthe collection, validation, processing of the user's PII, and sharingdata to facilitate transactions involving personal identifiableinformation of a specified user. The software implemented method mayfurther include expediting and facilitating the collection, validation,and processing of a user's PII and sharing data to facilitatetransactions involving PII of a specified user.

In some embodiments, a distributed virtual storage location over atleast one secure electronic location for the storage of the personalidentifiable information relevant to the specified user may beconfigured. The at least one secure electronic location may besynchronized with the electronic account such that the electronicaccount may control the at least one secure electronic location.

In some embodiments, the consumer may control the user's conditionaltiered storage such that the consumer's 2017 1040 tax return may bestored in an immutable storage media and/or in blockchain dataset wherelatency may not be a top priority. In contrast, the user's FaceBookand/or Linkedin data may be stored in Read/Write storage media that hasvery low latency and a security layer. In other embodiments, the usermay choose to have data stored in at least one particular location suchas their external hard drive, or cloud storage accounts such as GoogleDrive and/or MS OneDrive accounts, for example.

In some embodiments, the “profile” of the user, represented by a datastructure, may be generated in at least one format. The at least oneformat may include Raw Data. In a financial use case, for example, thismay be the information that a bank underwriter may read—this is what isoriginally aggregated, anonymized or pseudonymization where personalidentifiers may be masked, for example, as well as synthetic data, whichis a subset of anonymized data that may be machine generated.

In some embodiments, the data about the user may be obtained from rawdata about the consumer, extracted data from financial documentation ofthe specified user, extracted data from online accounts or providers,and/or data inputted from the user.

In some embodiments, the system for self-aggregation of personal dataand for self-aggregation of personal data of a user and personal datacustody, control, and stewardship may allow the user to maintain controlof the user's personal identifiable information, such that no one canaccess the user's PII unless the user first authorizes access to aperson, entity and/or any suitable third party. The self-aggregation ofthe user's PII from a plurality of electronic resources may includecollecting information from online accounts pertaining to the specifieduser. The system may further provide storing details of a manner ofverification that may include the name of entity providing verification,type of verification, and date of verification for each point of theuser's PII. Specifically, during the verification process, the systemmay record and maintain the manner of verification and date on which theinformation was verified. For example, “on 1 Feb. 2019, the 2018 1040IRS Tax return was verified by the Income Verification Express Service(IVES) Program”.

In some embodiments, an entity may include any individual, group ofpeople, non-profit organization, governmental organization, corporation,liability limited corporation (LLC), sole proprietor, and/or foreignentity that provides products and/or services as well as advice,governmental benefits, social benefits, third party benefits, forexample. The entity that may need specific data with PII of a user to beable to provide the user with products and/or services. The entity mayinclude a utility company, educational institution, healthcare company,insurance company, mortgage loan company, for example. The entity mayrefer to an approved provider in the marketplace, such that the entitymay receive the user's PII through an API (application processinginterface) or a YP Form Application Wizard.

In some embodiments, the entity may also include a provider user, whomay be an approved provider in the marketplace, such as consumerenterprises. The entity may also include commercial users that may notbe in the market place, but either may receive a data export and/or aqualified/targeted lead via an API or the YOURPII, such as toautomatically fill out the online form for the consumer, for example. Inother embodiments, a data export may include a qualified/targeted lead.

In some embodiments, the self-aggregation of the user's PII from theplurality of electronic resources may include uploading images or PDFfiles of financial documents, such as for example, bank and credit cardstatements, appraisal documents, home inspection documents, signedcontracts, disclosure forms, driver's license, tax returns, W-2 and 1099forms, pay stubs, and/or other financial-related documents.

In some embodiments, the method for the self-aggregation of personaldata and for self-aggregation of personal data of a user and personaldata custody, control, and stewardship may provide creating, in at leastone secure electronic location, multiple segments for storage of definedtypes of information each in a dedicated storage segment. For example,dedicated segments of data may be stored in electronic locations basedon the type of dedicated segment and/or other factors. The dedicatedsegments may include, for example, segments dedicated to income, assets,liabilities, financial statements, tax documents, completed loanapplications, validation, and/or ratio metrics.

In some embodiments, the method for the self-aggregation of personaldata and for self-aggregation of personal data of a user and personaldata custody, control, and stewardship may provide creating, in the atleast one secure electronic location, segments for data to be stored inimmutable storage media, or to be stored in mutable storage media basedon the nature or type of the information stored in the data. Immutabledata may include data that does not change, such as W2 and 1099 formsand tax returns. Mutable data may include variable data like a bankaccount or credit card current balance.

The dedicated segments may include segments for data to be stored in animmutable format (Write once/Read only Memory and/or a Blockchain). Thismay include data and/or objects related to historic information that maybe significant, such as income from a previous year. This may berepresented as an object such as a W2 form or 1040 form in a PDF datafile from a verified source, such as the Internal Revenue Service (IRS),or a digital version of a Verification of Employment from theiremployer. Some data, such as a bank account balance, may be stored in amutable format in a random-access memory (RAM), solid state drive (SSD),and/or a hard disk drive (HDD).

In some embodiments, in the same manner that the data is structured asdescribed above, there may be a similar need for segmenting the databased on data specifications which may be alternatively referred to asclassification (in laymen terms). For example, there may be a number ofdata specifications, including the Industry Loan Application Dataset(iLAD), which was just resealed by the Mortgage Industry StandardsMaintenance Organization (MISMO®), Uniform Loan Application Dataset(ULAD) developed by Government Sponsored Entities (GSE), for example,Fannie Mae, Freddie Mac, and Ginnie Mae other specification include analphabet soup MISMO, URLA, AUS, DU, LPA. Some data that was oncerecorded may not require updating (e.g. 1040 document). However, otherdata may need to be periodically updated (e.g., current bank balance).These different types of data may be classified accordingly, andsubsequently may be stored differently.

In some embodiments, metadata (tags) may be used to dynamicallydetermine how and where the data is stored as well as how the particulardata type/value may relate to a particular data schema. When data needsto be exported to a particular schema, YP may export or translate thedata to any standardized predefined data schema.

In some embodiments, the system may further classify the data accordingto various industry specifications. These industry specifications maydefine the syntax of XML or other data to make the data usable byvarious systems such that any third-party software may be able to readdata, for example, in a mortgage file. The standard FNMA mortgageapplication called the FNMA 1003, each data field in the applicationsuch as name, monthly income, years at current position, occupation,name of employer, for example, has a unique code which are defined inthese specifications, in addition to many other parameters, includingthe format/syntax of the data output.

In completing generic web form, such as a loan application, for example,there may be different scenarios. On the web, a lender may have anonline form as their loan application. In some embodiments, the YPplatform may automatically populate the fields so as to automatically“fill out” that form with the user's personal identifiable information.

In some embodiments, a mortgage lender may receive a full dataset of theuser's PII exported from the YP platform with the user's consent givenvia the YP platform. The user's PII data may then be relayed directlyinto the lender's Loan Origination system (LOS). For example, the annualincome value of $XX,XXX may be coded, labeled, and tagged per theindustry specifications, so LOS systems may automatically identify thisfield format as the annual income field value. The YP platform mayfurther relay information to the LOS system that this income value of$XX,XXX was verified by an IRS verification of a specific income serviceand/or some other verification methods. In some embodiments, some of theuser's PII may be coded, labeled, and/or tagged by the YP platform withmore granularly than the industry specifications, but there may be manydata points included in the industry specifications. The YP platform mayanalyze, segment, and/or store the data differently based on thesespecifications. In addition, the system may create a translator to mapthe user's PII coded, labeled, and/or tagged to any industryspecification.

In some embodiments, extracting and analyzing the data within thepersonal identifiable information may be based on specific predefinedcriteria.

In some embodiments, the method may include creating a optimized userinterface for the user based on data structure values (e.g.,demographic, psychographic, behavioral and/or quantitative user-specificparameters). Changes to the data structure values may generatecorresponding changes to the customized interface whenever necessary.

In some embodiments, the system may calculate financial ratios from theextracted data. Additionally, the system may calculate ratios of two ormore values that a third party commercial provider may deem valuable.Any other desirable ratios, formulae, and/or equations may be createdand, utilizing certain factors as assumptions (e.g., loan amount, loanterm), industry standard ratios may be generated and stored as syntheticdata.

In some embodiments, the extracted data may be used for loans andfinancial transactions. The extracted data may include bank and creditcard statements, tax returns, W-2 and 1099 forms, pay stubs, or otherfinancial-related documents.

In some embodiments, the extracted data may be inserted into pertinentfields on digital forms. In particular, the method involves insertingsaid extracted data into pertinent fields and populate this in an onlineform, or third-party software application through an API (applicationprocessing interface). Preferably, this involves dynamically exportingdata sets from the secure location or matrix for export to anApplication Programming Interface.

Additionally, the method for self-aggregation of personal data and forself-aggregation of personal data of a user and personal data custody,control, and stewardship may be used for prequalifying persons for afinancial transaction based on the analysis of the data. For example, anoutput of the above-mentioned analysis of the data and liquidity ratioassumptions (as described hereinbelow) may be needed if synthetic datais not used for prequalifying persons for a financial transaction.However, persons may be prequalified for a financial transaction basedon synthetic data that may include predetermined ratios, as describedhereinabove.

In some embodiments, the provider user may identify users that may“prequalify” for goods and services. If a Provider User such as aMortgage Lender may look for potential customers who have a Debt toIncome Ratio of Y % and a Liquidity Reserve of X. The system may searchfor a Unique ID # that is in both cohorts. Some of these values may bedynamic such that only the Liquidity Reserve Ratio may be calculated ifthe monthly mortgage payment and their total Liquid assets of potentialborrowers are known.

In some embodiments, the system may perform a query of a particularPIIMAP on the fly (known herein as a “Pre Tagged” method) even thoughthe value of the assumed monthly mortgage payment is not known. Thelender may want to use the system that may generate a standard unite.g., create a standard unit such as $1,000, if the Mortgage bank isassuming a monthly mortgage payment of $3,000. The mortgage back mayonly want to solicit a potential borrower that has 3 months of liquidityreserves, then the consumer user in the system with a score of 9 orhigher is desirable. There are 9 units of Value in their liquid assets,divided by 3 (because $3,000 is 3× the $1,000 unit) which equals 3, orthe number of months of reserves based on $3000 per month.

In some embodiments, the system may run many different scenarios such ashow long the potential borrower lived in their home, had a mortgage,debt to income ratio, have a car lease, had medical emergency and/orsurgery, watches certain streaming channels, etc. such that all theusers fitting each scenario. The system may create a “cohort”. Forexample, an exemplary cohort may be created from consumers that have anEstimated Debt to Income Ration of 30% to 35%, consumers with a“liquidity reserve” of 3 months of mortgage payments (e.g. their liquidassets equal 3 months of mortgage payments), and consumer having theirmortgage for over 5 years. Each Cohort may have a list of pointers to ainternal unique system ID for each consumer user that fits the cohortparameters. If the user provider has 3 parameters, they look for theconsumer users that are in all 3 cohorts.

In some embodiments, verifying information may be performed by thirdparties, government agencies, employers, banks, credit card companies,and/or verification service providers. The data values may includemetadata which may further include a date of input, a data source, and averification method used.

In some embodiments, the profile of the specified user may include age,gender, place of residence, spouse, occupation, employer, liabilities,credit score, income, current credit or loan obligations, and/or currentmortgage obligations.

The benefits to users and third parties using the computer-implementedmethod for self-aggregation of personal data and for self-aggregation ofpersonal data of a user and personal data custody, control, andstewardship as disclosed herein may include the following features:

1. A computer-implemented method for assisting a user [consumer] inidentifying and aggregating their personal identifiable information.

2. A computer-implemented method for a user to generate their owncomprehensive personal identifiable information that may include varioustypes of data including raw, anonymized and synthetic.

3. A computer-implemented method for securely storing data in differentenvironments based on data properties as defined by the users profileincluding mutable and immutable data and storage objects.

4. A computer-implemented method to personalize attributes of the userinterface and experience, such as general ergonomics and education, andpersonal identifiable information to determine factors, including devicetype, media type to use, subject matter, presenter, user interface (UI),user interface and experience (UIX), frequency, time and day reminders,assignment, tests and scoring.

5. A computer-implemented method for allowing the users to generatetheir own computer-implemented method for managing personal identifiableinformation e.g., raw, anonymized, and/or synthetic personalidentifiable information.

6. A computer-implemented method for assisting a user [consumer] inanalyzing or understanding their personal identifiable information.

7. A computer-implemented method of educating a user about what theirpersonal identifiable information implies about their financial profile,strengths, and/or weaknesses.

8. A computer-implemented method for managing personal identifiableinformation.

In the embodiments disclosed herein, users may control their own data.In addition, lenders may have cost effective, on-demand access to theuser's PII, and may opt-in to complete user data with validation. Thismay streamline and expedite the loan process, for example. Instead of aloan applicant repeating the same process with the same forms anddocuments, access may be granted to a perspective new lender for accessto that exact information. Thus, the entire loan process may besubstantially shortened and simplified.

In some embodiments, the consumer may grant the lender permissionedaccess at different levels for prequalification, then greater access atthe next stage for application. Regarding the consumer permissions(opt-in), the YP platform may allow the lender to have easy access tothe consumer data either via synthetic or anonymized profile matching,the consumer's specific permissioned access (opt in), and/or via aprepared preprocessed set of data. This may be a subset of the PIIMAPand/or PII PRINT for prequalification or a full set for loan applicationvalues, documents and verification data.

Furthermore, any data sharing between the YP platform and third-partycomputer systems may be based on an opt in profile filtered and refinedby a series of paired questions or any other suitable procedure. In someembodiments, certain synthetic data may be analyzed and relayed to thirdparties, and subject to permissioning, but the PII data is may begranted permissioned access based on a per diem basis, that the consumermay renew.

The YP platform may provide a central repository for a user's financialinformation and documents, which may be accessed by any number ofauthorized lenders or financial institutions. Any data sharing may bebased on the user's choice to share a specific set of information with aspecific person or enterprise. In other embodiments, the consumerpermissioned data, once granted permissioned, the 3rd party computersystem may choose to receive all or a part of the permission data havebeen permissioned (e.g., any subset thereof).

Thus, having applied the computer-implemented method forself-aggregation of personal data and for self-aggregation of personaldata of a user and personal data custody, control, and stewardship onceto fill out a first loan application, any subsequent loan applicationsmay be automatically filled out by the YP platform. The system mayenable a consumer to use a single, validated loan application and inputdata directly into a lender's loan origination system. The system mayenable banks to prequalify loan applicants with better quality data andless regulatory liability.

Stated differently, once the user's PII MAP has been sufficientlypopulated with PII data, the PII MAP on the YP platform forself-aggregation of personal data of a user and personal data custody,control, and stewardship for choosing what personal data to selectivelyrelay to third parties. The YP platform may provide a central safetrusted place to store all of the user's personal financial records. TheYP platform may be a source of personally tailored information, therebyassisting the user in increasing their financial literacy about loans,credit cards, banking, mortgages, taxes, budgeting and other financialmatters.

In some embodiments, the secure electronic location may calculatefinancial ratios from the extracted data. These ratios may be formattedaccordingly to data standards commonly used by financial institutions,for example, The Mortgage Industry Standards Maintenance Organization(MISMO), which is responsible for developing standards for exchanginginformation and conducting business in the U.S. mortgage financeindustry, created the Industry Loan Application Dataset (iLAD). Theseratios may be calculated on the Raw data or Anonymized data in the PIIMAP. The output may be a sum expressed as a ratio along with metadata(e.g., verification method, documentation type etc.) and, if required,certain assumed variables (e.g. Property value, new loan amount) in aformat according to various industry data standards (e.g. iLAD). ThisPII Print creates a synthetic data set for a specific consumer thatallows, in one instance, a mortgage lender to analyze verified documentprimary mortgage qualification ratios, without the lender needing tocome in direct contact with the consumer's PII data. PII Print may usemultiple assumptions and express various ratios for example:

In some embodiments, the YP platform may perform a PII Print of a commonunderwriting ratios output. The common underwriting ratios may be, forexample, when a loan amount has been specified by the borrower with anassumed interest rate and key ratios. Each key ratio may have a multiplesegment coded variation chain such as for example [Type 1/Date][Type2/Date]=[collaborated/rate as a %].

In some embodiments, the YP platform may perform a PII Print of a commonunderwriting ratios output. The common underwriting ratios may be, forexample, when a loan amount may be the current outstanding mortgagebalance and the interest rate is assumed and key ratios. Each Key ratiomay multiple segment coded variation chain such as for example, [Type1/Date] [Type 2/Date]=[collaborated/rate as a %].

In some embodiments, the YP platform may perform a PII Print of a commonunderwriting ratios output. The common underwriting ratios may be, forexample, when a loan amount and interest rate are assumed. Each Keyratio has multiple segment coded variation chain e.g. [Type 1/Date][Type 2/Date]=[collaborated/rate as a %].

In some embodiments, the YP platform may perform a PII Print of a commonunderwriting ratios output. The common underwriting ratios may be, forexample, when key ratio ranges are assumed [lender specific] and otherratios may be multiple segment coded validation chain e.g. [Type 1/Date][Type 2/Date]=[collaborated/rate as a %].

In some embodiments, a database used for the secure locations and thePII MAP may include a federated database system, which may furtherinclude distributed, multi-model, and deductive databases with directdata inputs, as well as images and/or PDF files either inputted directlyby the consumer, derived from documentation provided by consumer (e.g.,data and/or metadata), received from third party verification servicesauthorized by the user, generated by the user's use of the application,or any combination thereof. In other embodiments, an AI engine (e.g.,the AI/algorithm/rules module 43 as below) may validate the data bycross-collaboration of multiple data points within YourPII system as ameans of verification. In yet other embodiments, YourPII withpermissioned access by the consumer, may permit third-parties (such asPLAID or FISERV, for example) to verify data points of a consumer, whois a user of the YourPII system.

In some embodiments, the system may utilize URLA, ULAD and MISMOstandard datasets with OCR and artificial intelligence (AI) to extractdata from loan applications, bank and credit card statements, taxreturns, W-2 and 1099 forms, pay stubs, or other financial-relateddocuments. These data sources may be validated against third partieswith each process date stamped, and stored in a blockchain dataset(s),which may be further layered based on times enhancements. This layeringmay include consumer educational activities, preferences questions, andlevel of understanding

Although the embodiments disclosed herein for self-aggregation ofpersonal data and for self-aggregation of personal data of a user andpersonal data custody, control, and stewardship may be applied toexemplary use cases in the financial industry sector, this is not by wayof limitation of the embodiments of the present disclosure. Theembodiments may be equally applied to use cases in managing user PIIdata in medical records, gaming, digital advertising, in legal services,and/or in other industry verticals leveraging the use of a user's PII.Accordingly, the embodiments herein below refer to a more generic usagecase in the management of a user's PII which may be applied to anyindustry vertical using a user's PII.

In some embodiments, a Provider User may be providers of Good and/orservices that may be in the system. In the Financial Services industryuse case (e.g., Credit Card company or mortgage company), the system mayreceive anonymized data in varying amounts. First, a minimal amount forpre-screening, and then larger anonymized data for prequalification. Thefull application data may include data points for a loan application1003 FNMA, collaborative documentation (e.g. 1040 tax return, bankstatements etc.), Data/Document verification information, other metadata(e.g., demographic, psychographic, behavioral and/or quantitativeuser-specific parameters).

In some embodiments, for an advertising industry use case (e.g. Taboola,Amazon.com retail Website, Spotify, and ITunes), the system may receiveanonymized data in varying amounts: (i) a minimal amount of data may befor High Level Demographics and (ii) a larger set of anonymizedDemographic and Psychographic data. The system may receive their actualPII in various levels: (i) a minimal set of data may be for High LevelDemographics, (ii) a next level set of data including Demographic andPsychographic data, and (iii) a larger set of demographic,psychographic, and behavioral data (e.g., purchase history etc.).

In some embodiments, the service providers in various industries dealingwith consumer data may be recipients of the data that the consumer haspermissioned in addition to their role as data contributors, e.g.Experian. These service providers may receive self-reporting Data suchas PLAID. They may originate or validate data and can sell third-partyvalidation for a particular consumer file as well.

FIG. 1 depicts a system 1 for self-aggregation of personal data of auser and personal data custody, control, and stewardship in accordancewith one or more embodiments of the present disclosure. The system 1 mayinclude a server 15, a computing device 65 of a user 10, M third partyservers 20A, 20B, and 20C where M is an integer, and Q electronicresources 25A, 25B, 25C where Q is an integer all communicating 35 overa communication network 30.

In some embodiments, the computing device 65 of the user 10 is notlimited to a desktop computer as shown in FIG. 1, but may include alaptop, a tablet, a smartphone, a cellular phone, Alexa, wearabledevices, any suitable communication device, and the like.

In some embodiments, the computing device 65 of the user 10 may includea processor 66, a memory 75, input and output (I/O) devices 76 such as akeyboard 76B and a display 76A of the user 10 displaying a GUI 70, and acommunication circuitry 77 for communicating 35 over the communicationnetwork 30. The processor 66 may be configured to execute softwaremodules stored in the memory 75, such as the YOUR PII software platformand interface 80 a graphic user interface 70 displayed as GUI 70 ondisplay 76A.

In some embodiments, the server 15 may include a logic layer processor40, a memory and/or storage device 50, input and output (I/O) devices60, and/or a communication circuitry 55 for communicating 35 over thecommunication network 30. The logic layer processor 40 is a processorthat executes a logic layer software application.

In some embodiments, the logic layer processor 40 may be resident in anysuitable computing device and is not limited to the server 15 as shownin FIG. 1.

In some embodiments, YP platform may use any distributed storage systemand is not limited to storage on the server 15.

In some embodiments, the logic layer processor 40 of the server 15 mayexecute software modules stored in the memory 50 to perform thefunctions of the system 1 described herein. The software modules mayinclude a self-aggregation data module 41 for collecting and aggregatingPII data from the user 10 from the plurality of electronic resources20A, 20B, and 20C denoted ER1, ER2, . . . ERQ, a PII classifier module42 for classifying the PII data of the user 10 into types and/or classesof PII data, an artificial intelligence (AI), algorithm, and/or set ofrules module 43 for transforming the PII data of the user, a datastructure manager module 44 for building, updating and maintaining adata structure 6 and/or a PII MAP 5 of the user 10 based on the PII dataof the user 10, a plurality of data management software functions 45 forusing the PII data and the PII MAP 5 of the user 10 to perform many ofthe data management software functions described herein, a graphicaluser interface (GUI) manager 47 for controlling the GUI 70 and/or the YPplatform 80 running on the computing device 65 of the user 10, and/or anapplication programming interface (API) manager 48.

In some embodiments, the data structure 6 may be a distributed set offederated data. In other embodiments, the PII MAP 5 may be a matrix withtwo and/or three vectors.

In some embodiments, the memory 50 of the server 15 may store data foreach of the plurality of N users, where N is an integer. For example,the memory 50 may store for USER1 51 in secure electronic locations1 52for USER1 and a PII MAP1 53, and for USERN 54 in secure electroniclocationsN 57 for USERN and a PII MAPN 56.

Initial or Raw PII (e.g., received from the electronic resources 25A,25B, and 25C) for the user 10 and/or transformed PII after applying theartificial intelligence (AI), algorithm, and/or set of rules module 43(known herein after as the AI/algorithm/rules module 43) may be storedin either the at least one secure location 57 for USERN and/or PIIMAPNfor the Nth User. The PIIMAP (e.g., PIIMAP1 . . . PIIMAPN) may be storedlike objects and/or erasure coding (e.g., distributed storage).

It should be noted that although these secure electronic locations 57and/or PIIMAPN 56 may reside on the server 15, they may be located inany suitable storage location that may be at any remote location and/ormay be in distributed storage and/or may be located on differentcomputing systems that may accessible by the various software modules bythe logic layer processor 40 and/or element on the server 15 so as toperform the functions described herein.

In some embodiments, M third party servers 20A, 20B, and 20C denotedTPS1, TPS2, . . . TPSM where M is an integer associated with an entityand/or a person that may wish to receive PII of the user 10 withpermissioned access. In other embodiments, permissioned access may be aonetime access to a limited amount or may be a complete set of datavalues (e.g. ASCII formatted in a particular schema like ILDA), media(PDFs as in a 1040 tax form) as well as verification data and othermetadata.

In some embodiments, the third-party entity and/or person may need to doso through API if required denoted API1, API2 . . . APIN which may bemanaged and/or recognized by API manager 48 on the server 15.

In some embodiments, the plurality of electronic resources 25A, 25B, and25C may each a plurality of data elements. For example, ER1 may includedata element1 26A . . . data element1 27A where I is an integer, ER2 mayinclude data element1 26B . . . data elementJ 27B, where J is aninteger, and ERN may include data element1 26C . . . data elementK 27Cwhere K is an integer.

An electronic resource in the context used herein may refer to, but notlimited to a resource which require computer access or any electronicproduct that delivers a collection of data, be it text referring to fulltext bases, electronic journals, image collections, other multimediaproducts and numerical, graphical or time based, such as, for example,as a commercially available title that has been published with an aim tobeing marketed. An electronic resource in the context described hereinmay further refer to a resource in which a user's PII may be stored in aplurality of data elements. An electronic resource may include datatransfer project (DTP) data such as Facebook, Google, LinkedIn, etc.

In some embodiments, the plurality of data elements may include, but isnot limited to a US State Driver's license, a State ID, a US Passport, aforeign Passport, a foreign Driver's license, a Birth Certificate, aVoter Registration, a Hunting License, a Special Vehicle license, aMotor Vehicle Registration, Experience Data Model (XDM) System Data, aCross-device, a cross-platform customer journey tracking, a Purchasehistory, a browsing, a shopping cart, a calendar schedule of meetingsand events, a History of game play (including scores, awards, prizes,and/or levels achieved), a History of usage application, a browserhistory, a data Export in Data Transfer Protocol format, a play historyfrom a streaming media website, a Google Take Data Export, an IPaddress, a Browser type, Browser language settings, Screen size, Customsegments, Device type, Plugin details, an Internal Revenue Service (IRS)Form 1040, an IRS Form 1120, an IRS Form 1065, an IRS Form W2, an IRSForm 1099, Data, an IRS Transcript, a financial document, a bankstatement, a credit card (e.g., purchase history), digital forms ofcanceled checks, a statement, an appraisal document, a home inspectiondocument, a signed contract, a disclosure form, a pay stub, a UtilityBill, a consumer credit report, a consumer credit score, a data exportfrom a repository of consumer credit data using the Metro2 data schema,a Telecom bill, a financial-related document, a Transaction history, anX-ray, CGI, Medical Test results (Blood test results, Covid-19 testresults), or any combination thereof and metadata of these files (e.g.,data elements).

In some embodiments, the PII classifier 42 and/or the AI/algorithm/rulesmodule 43 may classified the data in accordance with a standardizedpredefined data schema and generating metadata that will be part of thePIIMAP. The metadata may enable a universal translation to anystandardized predefined data schema. The standardized predefined dataschema as used herein may refer to, but is not limited to data forimport/export via an API with specific formats (PDF, ASCII etc.) set byISO 13606:2019 XML Schema, Schema.org, Experience Data Model (XDM), MeF,FDX, OFX, DTP, Takeout, AIM, URLA, ULDA, ILDA, and times series data,Guideline Definition Language v2 (GDL2 SPECCDS-2, GEHR (The GEHR ObjectModel), ISO 19115, and/or DDI.

In some embodiments, for data exporting, the data may be taken andautomatically formatted to the standardized predefined data schema needfor a particular application that is being exported to.

FIG. 2 depicts a process flow 100 for the self-aggregation of a userpersonal identifiable information of a user in accordance with one ormore embodiments of the present disclosure. The steps herein below asdescribed in FIG. 2 may be performed by the logic layer processor 40,and more specifically by the logic layer processor 40 executing theself-aggregation data module 41, the PII classifier module 42, theAI/algorithm/Rules module 43, and/or the data structure manager 44 ofFIG. 1. In other embodiments, the PII classifier 42 and theAI/algorithm/Rules 43 modules may be the same module.

In some embodiments, an account of the user 10 on the YP platform may beopened and/or a system identification ID number may be generated in astep 102. This may occur in multiple ways such as for example: (i) inshort iterative responses such as name and then e-mail, (ii)simultaneous or sequential login through Linkedin, Facebook, Gmailaccounts, any social media accounts, and/or any email account, (iii)uploading a picture ID via a mobile Operational Capability ImprovementRequest (OCIR) with ID verification. Any mobile device and.or anycomputing device may use the verification services, which may be acloud-based service from a third party. Step 102 may include a defaultautomatic or opt-in user preferences for storage prompts. In otherembodiments, two-step verification and/or two-factor authentication maybe implemented. The user may designate where to store the data. The YPplatform may be accessed via a web browser running on the computingdevice 65 of the user 10 and/or by a client-side application (e.g., YPplatform 80) running on the computing device 65 of the user 10.

In some embodiments, after the account of the user 10 is opened, theself-aggregation module 41 may prompt the user 10 on GUI 70 to importtheir credit reports from suppliers of credit reporting data in a firstuser, third party data import step 104, such as the main U.S.-basedthree credit bureaus, e.g., Experian, Transunion, and Equifax. The PIIdata elements with this information may be imported directly from any ofthe plurality of electronic resources 25A, 25B and 25C associated withthe credit bureau repositories. The PII data elements may be importedfrom any of the plurality of electronic resources 25A, 25B and 25Cassociated with at least one vendor that provides data from any of theplurality of electronic resources 25A, 25B and 25C associated with thecredit bureau repositories. The PII data elements may be imported from awebsite supplying the credit information such as annualcreditreport.com,for example. This data may also include the verification sources of thePII data elements received any of the plurality of electronic resources.

In some embodiments, the AI/algorithm/rules module 43 may analyze in astep 106, the data aggregated from the first user third party dataimport step 104. A user-specific profile map, (e.g., PII MAP 5) that mayinclude a data structure based on the PII data elements using theinitial data from step 104 (e.g., data structure with initial raw data).In addition, the AI/algorithm/rules module 43 may further identify gapsin the received data, identify what other data may need to be imported,and/or to ask the user 10 on GUI 70 if the user 10 may have additionalaccounts with other third-party entities, for example. In otherembodiments, a personal profile crawler over the plurality of electronicresources 25A, 25B, and 25C may be used to identify the gaps in thereceived data.

In some embodiments, the self-aggregation module 41 may prompt the user10 on GUI 70 in a second user third party data import step 108, forexample, to extract PII data from websites of social media and/or emailwebsites such as, for example, Linkedin, Facebook, Google, Apple, and/orTwitter. In other embodiments, the open source initiative known as datatransfer project (DTP) may be used in acquiring this data.

In some embodiments, the AI/algorithm/rules module 43 may analyze thedata from the social media and/or email websites in a step 110, forexample, to apply modeling and/or statistical analyses extractingdemographic, lifestyle, psychographic and/or behavioral user-specificdata (e.g., parameters) from the user 10 and the data structure manager44 may further update the data structure of the PII MAP 5 with PII datatransformed by the AI/algorithm/rules module 43. The AI/algorithm/rulesmodule 43 may further identify gaps in the received data from step 108and identify what other data may need to be imported, and/or to ask theuser 10 on GUI 70 if the user 10 may have additional accounts with otherthird-party entities (additional social media and/or email accounts),for example.

In some embodiments, the self-aggregation module 41 may receivequantitative data (e.g., parameters) by prompting the user 10 on GUI 70in a third user third party data import step 112, for example, to loginto multiple financial accounts, such as bank account, loan accounts,and/or credit card accounts to extract PII data. In other embodiments,an application such as Akoya, for example, may be used to downloadfinancial statements and spreadsheets with account activity data in CSVformat, for example.

In some embodiments, demographic user-specific parameters may includegender, DOB (age group), marital status, education, occupation, and/orfamily structure, etc of the user 10.

In some embodiments, psychographic user-specific parameter may includeCategory Recency/Frequency/Monetary Value (RFM), lifestyles, opinions,attitudes, and beliefs, personality traits, psychological responses,social trends, AM/PM Personality Profile, Myers-Briggs Type Indicator(MBTI), Minnesota Multiphasic Personality Inventory (MMPI), DiSCpersonality profile, The 16 Personality Factor Questionnaire (16PF),and/or HEXACO Model of Personality Structure Personality Inventory ofthe user 10.

In some embodiments, behavioral user-specific parameters may includeactivity, interest, opinion (AIOs), attitudes, values, and/or behaviorof the user 10.

In some embodiments, quantitative user-specific parameters may includespecific values relating to usage history, product ownership, medicalhistory, financial history, etc. of the user 10.

In some embodiments, the AI/algorithm/rules module 43 may analyze thequantitative data (e.g., the financial data) in a step 114. Liabilitiesof the user 10 may be extracted from the credit reports and/or thequantitative data. The AI/algorithm/rules module 43 may further identifygaps in the received data from step 112 and identify what other data mayneed to be imported, and/or to ask the user 10 on GUI 70 if the user 10may have additional financial accounts with other third-party entities,for example. The data structure manager 44 may then update the PII MAP5.

In some embodiments, the self-aggregation module 41 may receivequantitative data by prompting the user 10 on GUI 70 in a fourth userthird party data import step 116. A prompt on GUI 70 may direct the user10 to receive more PII data elements from another third party such as acertified public accountant (CPA), for example. In other embodiments,the user 10 may designate through the GUI 70 for the self-aggregationmodule 41 additional data sources and documentation from which toreceive PII data elements. These additional data sources anddocumentation may be deemed important by the YP system, independent ofthe user 10 for building the PII MAP 5 of the user 10. For example, theuser 10 may have previously saved financial statements, for example, orother documents to upload for the PII MAP 5.

In some embodiments, the AI/algorithm/rules module 43 may analyze thedata in the additional documents (e.g., from the step 116) for updatingthe PII data structure of the PII MAP 5 in a step 118. TheAI/algorithm/rules module 43 may further identify gaps in the receiveddata from step 112 and identify what other data may need to be imported,and/or to ask the user 10 on GUI 70 if the user 10 may have additionalfinancial accounts with other third-party entities, for example.

In some embodiments, this iterative flow shown in FIG. 2 may continue ntimes where n is an integer to self-aggregate and analyze the PII dataof the user 10. The self-aggregation module 41 may receive PII data byautomatically, by prompting the user 10 on GUI 70 in an n-th user thirdparty data import step 120, and/or by the user 10 designating datasources and documentation to use in the PII data structure in a step120. This data from n-th user third party data import (e.g., the step120) may be applied to the AI/algorithm/rules module 43 to transform thePII data of the user which the data structure manager 44 uses to updatethe PII data structure of the PII MAP 5 in a step 122.

In some embodiments, this iterative flow may continue until a PII datastructure with sufficient granularity may include a sufficientlypopulated set of data in step 124. The data structure manager 44 maydetermine when the user-specific profile map crosses any suitablepredefined data threshold in terms of a threshold amount of PII in thePIIMAP 5. In this case, the user-specific profile map may be referred toas a user-specific threshold profile map. The threshold may vary basedon the user's profile. Some factors may include but are not limited todemographic factors, psychographic factors, behavioral factors, andcurrent best practices in UI design psychology including (i) decisionfatigue, (ii) the Fogg B-Map, (iii) gamification, (iv) behavioral designand (v) other cognitive psychology factors that come in effect with thefield of interface design.

In some embodiments, the AI/algorithm/rules module 43 may determine thebalance between starting to populate the PIIMAP 5 with as much data aspossible, the user effort required, the user levels of motivation andthe probability of sustaining that motivation.

FIGS. 3A and 3B depict a first threshold PII MAP 126 and a secondthreshold PII MAP 128 in accordance with one or more embodiments of thepresent disclosure. The first threshold PII map for a user 10 referredto as user1 125 that may be a senior citizen for example. User1 125 mayhave a lower score or a lower threshold such that data may need to beadded to the PIIMAP 5 in later iterations. The second threshold PII map128 for a user 10 referred to as user2 127 that may be a young man, forexample. User2 127 may have a higher score or a higher threshold suchthat the user2 may be able to tolerate the time and effort to collectmore data in the initial setup.

FIG. 4 depicts a graphical representation 130 of data types used in thepersonal identifiable information map (e.g., the PII MAP 5) of the user10 in accordance with one or more embodiments of the present disclosure.The data types used in the PII MAP 5 of the PII of the user 10 as shownin the graphical representation 130 may include an identification (ID)132, income data 134, a credit history 136, assets 138, liabilities 140,modeled profiles 142 (such as demographic, psychometric, etc), data fromsocial websites 144 (e.g., demographic, psychometric, etc.), ratiometrics 146, and usage history 147. The PII MAP 5 data types may alsoinclude validation sources and descriptions 148 for the data with theuser's PII as shown in the dashed lines. Security decoy data 150dispersed throughout the PIIMAP (not shown in FIG. 4) may be an outerband 0 in the graphical representation 130 that may represent a computersecurity mechanism set to detect, deflect, or counteract attempts atunauthorized use of the systems shown herein. Approaching the center ofthe graphical representation 130, access to the data becomes morerestrictive.

In some embodiments, data from the aggregation steps of the logic layerprocessor 40 receiving PII from credit reporting websites (e.g., step104) and PII from social media websites (e.g., step 108) as previouslydescribed above may be analyzed by the PII classifier module 42. Inother embodiments, the PII classifier module 42 may include a classifiermachine learning model. In yet other embodiments, the functionality ofthe PII classifier module 42 to classify the user's PII into data typesmay be bundled into and performed by the AI/algorithm/rules module 43.

In some embodiments, the AI/algorithm/rules module 43 may analyze theaggregated data. A data structure (e.g., an initial build of the userprofile) may be generated with raw data with PII of the user 10. Thesections of data structure may be based on specified predetermined dataschema which may include multiple score values whether if populated ornot, and reliability of the analysis of data from steps 104 and 108 soas to build a user profile of age, gender, place of residence,occupation, employer, liabilities, credit score, and/or income currentmortgage, for example.

In some embodiments, the AI/algorithm/rules module 43 may generate alist of user accounts (e.g. gaps in the profile data that may be ratedby probability) that may have not been identified. The list may be anactual list or a proposed list that is based on probabilities andassessments from the AI/algorithm/rules module 43 after analyzing otherfactors.

In some embodiments, the AI/algorithm/rules module 43 may perform ananalysis based on current User Profile AI that assesses the highestprobabilities for the type of User Interface and User Experience (UIX)build, essentially a behavioral design of an optimal UIX, that willenable the user to get the most out of the YP platform with the leasteffort on the user's part. Optional configurations may be outputted tothe user 10 which may be displayed on the GUI 70 as choices that theuser 10 may define choice best for the user. The UIX may be generatedbased on AI/algorithm/rules module 43 and the User input.

In some embodiments, another process flow of for populating the datastructure and developing PII machine automated presentation is asfollows: the AI/algorithm/rules module 43 may identify missing PII datapoints from the received PII data, which may be outputted to the user 10on the GUI 70 using multiple choice or dropdown menus. This gap analysismay be performed by the AI/algorithm/rules module 43 to identify anumber of options that optimize the intersection for graphing an optimalUIX for inputting missing data. The AI/algorithm/rules module 43 mayprovide balancing that is least disruptive to the user 10 while stillmaintaining data integrity and comprehensiveness. This may need anincremental process to achieve the data integrity and comprehensivenesswhile maintaining the optimal UIX.

Note that the term optimal UIX as used herein may refer to a UIX thatutilizes factors in Ethical Behavioral Design science which utilizesindividual consumer attributes as derived from various functions in theYP platform including demographic, psychographic, cognitive, behavioral,and quantitative factors so as to generate a user experience that isboth optimized and personalized for the particular user.

With the users' authorization, multiple data sources may be identifiedthrough the YP platform 80 through a personal profile crawler thatutilizes the AI/algorithm/rules module 43 in the PII Print applicationto identify financial account data sources [e.g. a bank credit card onthe credit report, or the 1099 interest reported.

In some embodiments, the user 10 may be presented with the identifiedmissing data points based on a priority set generated by theAI/algorithm/rules module 43 based on the data structure analysis. Theuser 10 may be given a few options to choose from for adding orenhancing existing data. In other embodiments, the user 10 may choose anoption “Identified For User” to allow the AI/algorithm/rules module 43to make a final choice for the user 10.

In some embodiments, the user interface (UI) such as the GUI 70 for datainput may be generated. Data may be inputted from a user's own onlineaccount, the web, files, an upload, or from a third party.

In some embodiments, the user 10 may choose at any time to continue dataaggregation at another time, where they left off, to send a reminder,and/or to create join reminder group.

In some embodiments, the user 10 may choose a data aggregation optionfor each data point. The user 10 may include the same option for some orall the options. For example, the user may aggregate the user's own datafrom each online account. (i) For example, the client may log in to theuser's bank account using the same browser with the same login user nameas the user's YP account (hereinafter YP). The YP Application mayinclude options for downloading PDF statements, screen scrape accountinformation from browser, and download data. (ii) The AI/algorithm/rulesmodule 43 may analyze and classify the data. For example, the logiclayer processor 40 may encrypt the data if identified as highlydescriptive sensitive financial data such as a bank statement based onclassifications from the AI/algorithm/rules module 43. In otherembodiments, the classification may also include a determination of thestorage type and security to be used. If the bank statement is in a PDFformat, the bank statement may be stored as immutable encrypteddistributed storage. A W2 or 1099 form from a prior year may be storedin immutable encrypted distributed storage.

In some embodiments, the user 10 may choose to aggregate the user's owndata from local files stored on the user's computing device 65 and/orthe user may set reminders to remind the user 10 to self-aggregate datafrom another device (e.g. on the user's mobile device which may be set areminder to retrieve a PDF file with their 1040 from the laptop ortablet). The UI (e.g., the GUI 70) may guide the user 10 to classifyfiles. The GUI 70 may allow the user 10 to choose a form such as a 1040tax return, to choose a year from a dropdown menu depending on UIX buildabove. The AI/algorithm/rules module 43 may extract data elements fromthe document with PII and may confirm with the user 10.

In some embodiments, the user 10 may authorize or enable a third-partysource to upload data, such as the accountant (CPA) of the user 10.Enabling and/or disabling may be done with any third parties designatedby the user 10.

In some embodiments, the AI/algorithm/rules module 43 may generate thedata structure for the PII MAP 5 and multiple data set types (i.e. RAW,Anonymized, Synthetic). The AI/algorithm/rules module 43 may be appliedto the data structure to analyze data based on Demographic, Behavioral,psychometric, psychographic user-specific parameters, which thenpopulates those data points in the PIIMAP 5.

In some embodiments, the AI/algorithm/rules module 43 may analyze databased on various industry data standards, schemas, and/or syntaxes. Datatags such as income, debt, payment obligations, liquid assets,non-liquid assets, direct liabilities, contingent liabilities, and otherdata relating directly or indirectly to financial factors may be appliedto the data. Tags may be metadata, or a data value expressed in aparticular manner. Tags may include one or more standards from industry,NGO (organizations or open source) and or Government agencies, includingUniform Loan Application Dataset (ULAD), Uniform Residential LoanApplication (URLA), The Mortgage Industry Standards MaintenanceOrganization (MISMO), eXtensible Business Reporting Language (XBRL),Object Management Group® (OMG®) and the like.

In some embodiments, the AI/algorithm/rules module 43 may be used insteps 104 and 108 above so as to generate a higher resolution datastructure such as a two and/or three vector matrix that may includeself-contained subsets. Each point in these matrices (e.g., datastructure element) may represent a unique value including:

-   -   a. An element value (e.g. date of birth DOB)    -   b. How the values of two elements are related and expressed        mathematically. For example, it may be the sum of one element        value divided by another or another factor such as annual debt        payments divided by annual income, for example.    -   c. The data source and/or type of a particular elemental value        (e.g. DOB retrieved from Driver's License (DL))    -   d. The date of the relevant data source and/or type of a        particular elemental value (e.g., a valid DL or if an elemental        data value is dynamic, e.g. a bank account balance that may        change periodically with the month and day of the statement).    -   e. A binary factor—even if a data structure element or point        (e.g. “d” above) may still be valid, such as an 8-month-old bank        statement. The 8-month old bank statement may not be relevant by        itself. However, a current statement with two previous        statements may show a trend, whereas the 8-month old bank        statement may not be valid on a standalone basis.    -   f. A score value rating indicative of the data source validating        factor value (e.g. “d” above). The data source with a lower        score may be assigned to a copy of a paycheck as a validation        for the source of income. However, income data retrieved from        the IRS website or another trusted third party may have a higher        score value.    -   g. Demographic elemental values such as Gender and/or in “c”        above    -   h. Demographic values relating to the current quality, age, or        presence in the data structure elemental values (like “d” above)        which is true for all elemental data types.    -   i. Psychographic elemental values based on social issues that        the individual may have expressed interest, based on what was        last retrieved from Social Media (step 108) and/or spending        habits (step 112) based on analysis of credit card or bank        account statements.    -   j. Psychographic values related to the current quality, age, or        presence in the data structure elemental values (like “d” above)    -   k. Behavioral elemental values (based on user's usage history of        the method, or retrieved from Social Media, data from analysis        of credit card or bank account statements, responses to requests        for direct user input (step 116), which may be binary, multiple        choice or direct field input from the GUI 70.    -   l. Behavioral values relating to the current quality, age, or        presence in the data structure elemental values (like “d”        above).

FIG. 5 depicts an exemplary representation 160 of the plurality of datamanagement software functions 45 in accordance with one or moreembodiments of the present disclosure. The logic layer processor 40 is aprocessor that executes a logic layer software application. The logiclayer software application may include, in part, the plurality of datamanagement software functions 45. The logic layer processor 40 mayprocess the incoming (initial) user PII, may generate the PII MAP, andmay process an iterative PII MAP with additional received user PII withthe PIIMAP including demographic parameters, psychographic parameters,behavioral user-specific parameters, and/or quantitative parameters. Thelogic layer processor 40 may manage interactions with the consumer user.The logic layer processor 40 may use the plurality of data managementsoftware functions 45 for a variety of functions described herein below.

In some embodiments, the plurality of data management software functions45 may include:

(1) Function—System Management Expert System Layer 162

(2) Function for extracting Demographic information 164(3) Function for extracting Psychographic information 166(4) Function for generating UI responsive elements 168(5) Function for extracting financial information 170(6) Function for extracting cognitive information 172(7) Function for extracting behavioral information 174(8) Function for generating a gamification UI 176(9) Function for providing financial literacy education 178(10) Function for setting storage format 180(11) Function for extracting risk information 182(12) Function for generating user recommendations 186(13) Function for setting storage medium 188(14) Function for data anonymization and security 190(15) Function for setting user-defined opt-in permission 192(16) Functions for importing and update data 194(17) Functions for assimilation and analysis 196 (if function 194 used)(18) Functions for generating ratio metrics A 198(19) Functions for generating ratio metrics B 200(20) Functions for generating ratio metrics C 202(21) Function for anonymization security and opt-in layer 204(22) N functions for Autonomous Agent 1 (system) 206 to Autonomous AgentN (system) 208(23) N functions for Autonomous Agent 1 (custom) 209 to Autonomous AgentN (custom) 210(24) N functions for Expert System #1 (system) 212 to Expert System #N(system) 214(25) N functions for Expert System #1 (custom) 216 to Expert System #1(custom) 218

In some embodiments, with regard to the function for System ManagementExpert System Layer 162, each expert system may read and analyze basedon the respective data types using the entire body of data of the PIIMAP as well as inferred values based on the inter-relationship of thedata. When the Expert system is able to extract a new value/data point,the PIIMAP may be updated.

In some embodiments, the logic layer processor 40 may execute functionswhen new data may be imported and or received that may include thefunction for extracting Demographic information 164, the function forextracting Psychographic information 166, the function for extractingfinancial information 170, the function for extracting cognitiveinformation 172, and the function for extracting behavioral information174. The function for extracting Demographic information 164 mayidentify data points with demographic value and may extract, forexample, Home Address, Age, marital status etc. The function forextracting Psychographic information 166 may identify data points withpsychographic value such as identifying data points and extracting, forexample, luxury purchases, affiliations, etc. The function forextracting financial information 170 may identify and extract financialdata, such as for example, asset values, liabilities, annual income,etc. The functions for extracting cognitive information 172 andbehavioral information 174 may identify data points with Behavioral andcognitive value and may extract data such as for example, purchasing andviewing history etc.

In some embodiments, with regard to the function for providing financialliteracy education 178, the logic layer processor 40 may use otherfunction's (demographic, psychographic, cognitive, behavioral,quantitative) in conjunction with the UIX logic engine and gamificationengine 176, that may include one or more expert systems 212 to 218. Thefunction for providing financial literacy education 178 may apply theseutilizing other YP system elements that may include taking into accountthe consumers' financial detail specifics and usage history so as toenable the user to learn more about subject matter pertinent to theirpersonal financial condition and that are uniquely personalized and ofinterest to them. This will be used by the system as well as madeavailable to third-parties (e.g., consumer permission-based).

In some embodiments, with regard to the function for setting storageformat 180, the logic layer processor 40, based on identifying variouscharacteristics of data and media/files imported, may set and specifythe storage device to one or more storage types in one or more locationswhich may be used to store the content.

In some embodiments, with regard to the function for extracting riskinformation 182, the logic layer processor 40 may use theAI/algorithm/rules module 43 to identify various types ofinter-relationships between data points in a generic way with apredefined standard unit of measure or value. The analysis of one ormore data points in the PIIMAP may be against each other or externaldata points. The output of that analysis may be stored as an additionaldata point in the PIIMAP and/or or as an anonymous identifier or underthe user's name (based on permissioning) within a cohort of similaranalysis factors, types, or values.

In some embodiments, with regard to the function for generating userrecommendations 186, the system may be able to confirm a user'sinteraction with a vendor. The user may be able to post a recommendationthat may be available to individuals, groups, or general, per the user'schoice. The user may have the choice of doing it anonymously or undertheir name. Moreover, the user may choose to make their recommendationanonymous for one group and to have their name appear in another.Depending on the subject matter, YP may only make the recommendationavailable to other consumers in the YP with similar mean attributes.

In some embodiments, with regard to the function for setting storagemedium 188, the logic layer processor 40 may specify, based onidentifying various characteristics of data and media/files imported,storage of one or more types and in one or more storage locations, whichmay be used to store the content.

In some embodiments, with regard to the function for data anonymizationand security 190, the logic layer processor 40, based on identifyingvarious characteristics of the raw data, metadata, files, or mediaimported, may determine the level and type of anonymization and accesssecurity.

In some embodiments, with regard to the function for settinguser-defined opt-in permission 192, the logic layer processor 40 (e.g.,YourPII), by using the factors (demographic, psychographic, cognitive,and behavioral) in conjunction with the UIX logic engine andgamification engine, may inform the user of their choices foropt-in/opt-out, and educate them about the ramifications ofpermissioning or not permissioning data in each specific situation. Theuser may be able to access these explanations at any time, as well aschange their settings.

In some embodiments, with regard to the function for importing andupdate data 194, the logic layer processor 40 may control the automationof identifying data types as well as specific data locations for import.This may be done on an automated basis by YourPII in the background. Itmay be done through prompts to the user or as a reaction to an input bythe user. Any time a user's input may be needed through (using thefunctionality of) the functions, the UIX logic engine and gamificationengine may generate the user interface that may optimize and align thelevel of effort, motivation, ability, and prompts for the user.

In some embodiments, with regard to the function for analysis andassimilation 196 (if function 194 is used), the logic layer processor 40may analyze data, files, and/or media, as well as existing raw data. TheAI/algorithm/rules module 43 may to generate additional YourPII MAP datapoints including but not limited to demographic, psychographic,cognitive, behavioral, and quantitative data points. This process mayhappen upon data, file, and/or media import, as well as on an ongoingbasis to all the data in all YP data types (raw data, PIIMAP, PII Print,etc.) in the YourPII system.

In some embodiments, with regard to the function for generating ratiometrics A 198, the logic layer processor 40 may generate ratio metricsbased on a library of values and or other YP functions (includingautonomous agents and Expert Systems) by using the functions includingdemographic, psychographic, cognitive, behavioral, and quantitative inconjunction with the UIX logic engine. A provider-user (e.g., a providerof goods or services to the consumer) may be provided a UI where theymay define the key ratios that they use in pre-qualifying potentialcustomers or in the case of government or NGOs, pre-qualifying potentialrecipients. This may generate a query that will identify all the[consumer] users (subject to consumer permissioning) on the YP systemthat match their requirements. The result of this query may be in theform of internal YP functions that prompt offers to the [consumer] user(subject to consumer permissioning) or that will generate a list ofquery matches (the quantity of which may be total or limited to apredefined amount). The list may be available only in the YP systemand/or as an exportable list for the provider-user, subject to consumerpermissioning, and the provider-user rights on the system. This may be aone-time action or may create an autonomous agent that may perform thison a recurring basis, which may be unique to the provider-user and mayrun on an automated or on-demand basis per the provider-user's choice.This description of the function for generating ratio metrics A 198 isidentical to the function for generating ratio metrics B 200, and to thefunction for generating ratio metrics C 202.

In some embodiments, with regard to the function for anonymizationsecurity and opt-in layer 204, the logic layer processor 40 may managelevels and types of data anonymization, storage and access security inthe system.

In some embodiments, with regard to the N functions for Autonomous Agent1 (system) 206 to Autonomous Agent N (system) 208, these may beautonomous agents that may be called to function by other parts of theYP system. This autonomous agent may run analyses in the systemutilizing any of the AI/algorithm/rules, functions (e.g., demographic,psychographic, cognitive, behavioral, and quantitative) and expertsystems in YP, so as to analyze individual profiles, user cohorts, ortotal user population for other system functionality.

In some embodiments, with regard to the N functions for Autonomous Agent1 (custom) 209 to Autonomous Agent N (custom) 210, these may beautonomous agents that can run analyses in the system based on inputsfrom a provider-user (e.g., provider of goods or services to theconsumer) utilizing any of the AI/algorithm/rules, functions (e.g.demographic, psychographic, cognitive, behavioral, and quantitative) andexpert systems in YP, so as to analyze individual profiles, usercohorts, or total user population for other system functionality.

In some embodiments, with regard to the N functions for Expert System #1(system) 212 to Expert System #N (system) 214, the logic layer processor40 may execute a program internal to the YP system that usesAI/algorithm/rules and may include a set of values or logic forms, forone or more subject matter areas, and may utilize other resources in YP,including other expert systems, and any of the plurality of datamanagement software functions 45 described hereinabove. The internalprogram may be used by the YP system to monitor actions, events, ordata, both internal and external, be used by other functions in the YPsystem for complex queries, or coordinate processes between YP systemfunctions, prompt or configure other systems in YP, to identify certainuser, system or third-party actions and conditions, to determineresponse to those or other events (including but not limited totime-related factors) and to determine or call a predetermined response.

In some embodiments, with regard to the N functions for Expert System #1(custom) 216 to Expert System #1 (custom) 218, the logic layer processor40 may execute a program that uses the AI/algorithm/rules module 43 andmay include a set of values or logic forms, for one or more subjectmatter areas that are defined or provided by a 3rd party or externalsources (including a provider-user [provider of goods or services to theconsumer]), and utilize other resources in YP, including other expertsystems, and any of the plurality of data management software functions45 described hereinabove. The program may be used by the YP system tomonitor actions, events, or data, both internal and external, be used byother functions in the YP system for complex queries, or coordinateprocesses between YP system functions, prompt or configure other systemsin YP, to identify certain user, system or third-party actions andconditions, to determine response to those or other events (includingbut not limited to time-related factors) and to determine or call apredetermined response.

A number of process flows for defining certain functionalities are nowdescribed herein below using the plurality of data management softwarefunctions 45.

In some embodiments, the logic layer processor 40 may generate a primaryUIX Build, which may be generated earlier in the process and iterativelypost the initial build. A customized user interface and experience maybe generated for the user 10 based on the input of certain datastructure values. These factors may include:

(1) Demographic factors

(2) Psychographic factors

(3) Behavioral factors

(4) Current best practices in UI design including (i) Decision Fatigueand (ii) other cognitive psychology factors that comes in effect withthe field of interface design

In some embodiments based on the results of generating the primary UIXbuild, the logic layer processor 40 may choose a library of UI themes,logics, and graphical elements. These UI themes, logics, and graphicelements may be part of a YP generated library or generated by thirdparties. This may include elements from well-known designers at a costif the user chooses.

FIGS. 6A and 6B depict a first exemplary screenshot 220 and a secondexemplary screenshot 222 of a graphic user interface in accordance withone or more embodiments of the present disclosure. These screenshotsillustrate the function for generating UI responsive elements 168 forthe user1 125 and for the user2 127 as described in FIGS. 3A and 3B. Forexample, generated UI elements may include an online form that willgenerate a next set of questions for the users based on the userresponses to a previous set of questions, so as to enable an optimalUIX. The PIIMAP 5 may be used upon invoking function 168 to generate auser-specific profile interface tailored to the user1 125 and the user2127. For example, the user1 may receive the GUI 70 that shows clickableicons 230 and/or slide bars 232 on the display 76A. Similarly, user2 127may receive the GUI 70 that shows app icons 233 to press on atouchscreen of a smartphone, for example.

In some embodiments, GUI 70 may also display to the user 10, a optimalUIX on the GUI 70 which may allow the user to grant access rights to aportion of the PII data to an entity and/or person. The portion of theuser's PII may refer to any extracted subset of the PIIMAP 5 that isgranted access to be made readable or exportable by a person and/orentity that the user granted access rights/permissions. Theuser-specific profile interface on the GUI 70 may also allow the user tofurther adjust data and may signify the output data for a loanapplication, for example.

In some embodiments, the UI/UX elements may include but are not limitedto Device Type, Device Model, Device configuration, OS, Screen size,Screen Resolution, Audio, Video, Eye tracking: Input Controls,Navigation Components, Informational components, Containers, Card,Breadcrumb, Bento Menu, Dropdown Checkbox, Kebab Menu, Input Field,Icon, Feed, Accordion, Button, Carousel, Comment, Winer Menu, Loader,Meatballs Menu, Modal, Notification, Pagination, Picker, Progress Bar,Radio Buttons, Search Field, Sidebar, Slider Controls, Stepper, Tag, TabBar, Tooltip, Toggle, icons, switches, click buttons etc. on the GUI 70that allows the user to manipulate PII data that may be transformed bythe AI/algorithm/rules module 43, for example.

In some embodiments, the user 10 may be provided with a series of binarychoices to refine and check the user interface and experiencedefinitions. The logic layer processor 40 may apply supervised orunsupervised machine learning here (e.g., in the AI/algorithm/rulesmodule 43) by utilizing factors in behavioral design science, and byutilizing individual consumer attributes as derived from variousfunctions in YP including demographic, psychographic, cognitive,behavioral, and quantitative factors to create an unique personalizedoptimization of the UIX.

In some embodiments, based on the user's choices, a UI configuration(e.g., the GUI 70) may be generated and presented as the UI of theUser's YourPII application.

In some embodiments, the user 10 may accept, or recreate the UIconfiguration (e.g., go through the prior steps again now or at a latertime). Reminders may be scheduled based on multiple factors includingpsychographics.

In some embodiments, the AI/algorithm/rules module 43 may use each setof results to help train the supervised or unsupervised machine learningmodels. The User 10 may be asked if user wants to become an activeparticipant in a test group.

In some embodiments, the user interface and experience may allow theuser to skip providing multiple-choice user input to create the UI. Thelogic layer processor 40 may iterate the UIX based on parametersdetermined in the data structure utilizing factors in behavioral designscience, and utilizing individual consumer attributes as derived fromvarious functions in YP including demographic, psychographic, cognitive,behavioral, and quantitative factors.

In some embodiments, the logic layer processor 40 may provide an ongoingupdate and/or enhancement of the PII MAP 5 and all elements generatedfrom it. The logic layer processor 40 may identify what data may bemissing from the PII Print. The logic layer processor 40 maycontinuously identify missing data and/or values within the user's datastructures. These missing values may be prioritized by various valuethat may be dynamic including the intent of the user whether that hasbeen stated or has been derived from the AI.

In some embodiments, the user 10 may be guided (depending on the UIXdefined in the previous processes), which may include explaining whatfactors are missing, why the particular factors are important, and howto add them to the data structure. This may include inputting datathrough the personalized UI (via icons, dropdowns, field fill, and/orany suitable graphic element), from a local drive or device, athird-party site, or any other site.

In some embodiments, the user 10 may decide to add a validation type(e.g. from a third-party service) of any particular data point. Multiplevalidations of the same data point may be used such as, for example, aprior years' annual income from the user's accountant (CPA) and from theIRS.

In some embodiments, a synthetic Data PII data structure may begenerated thus providing a trusted data source with data analysiswithout touching on Regulatory categorized PII.

In some embodiments, the AI/algorithm/rules module 43 may generate analternative data structure extracting certain data from the user's datastructure. The extracted data structure may only include synthetic datavalues based on data schemas such as iLAD, ULAD, MISMO as well as otherdataset specifications.

In some embodiments, the AI/algorithm/rules module 43 in generating theSynthetic data may make an analysis to classify the certain values inthe user's data structure to determine the values of certain assumptionsthat it will use to generate the synthetic data. For example, theAI/algorithm/rules module 43 may calculate based on the Valueassumptions and the user PII Print values utilizing various standardindustry ratios. For example, for Mortgage lending, qualification ratiosmay be used such as PITI-Housing Debt/Payment to income.

In some embodiments, a type A1-A table(s) of Ratio values may beoutputted based on existing (and historical) factors. A Syntheticrepresentation of the PII Print may used for prequalifying a user withinthe platform that has access to the assumption value keys. Regarding theinput for generating the Synthetic representation of the PII Print, thevalue assumptions may be replaced.

In some embodiments, new loan assumptions (e.g. principle, Amortizationand interest) may be used in the analysis to calculate valuesrepresenting Key lending qualification ratios. For this particular case,the extracted data structure that was generated may only be useable to athird party, provided authorization by the user 10, who had certainassumptions used. The output may a type X1-Xn table(s) of Ratio valuesbased on theoretical factors. Data Tables may be held in separatecontainers (or another secure architecture). A zero-trust method (orother secure architecture) may be used to identify matching profile tothe candidate profile that a potential provider is looking for.

In some embodiments, the user 10 via the YP platform may provide a userinput interface for third party providers. The user (consumer) mayaugment the user's data held by a third party, such as for example, theuser may add an explanation to their credit history held by a creditrepository. The user may upload data to their CPA's software. Theinterface may provide the user data needed to automatically completeloan applications, and to automatically completing a government benefitsapplication. The interface may provide the user data needed for onboarding with a certified financial planner (CFP), and/or a wealthplanner.

In some embodiments (e.g., a daily consumer usage example), the UI mayallow the Consumer User to opt in to a credit provider request, and/orto find a provider of services in the market place and to engage them.The UI may provide Friend recommendations and matched user Reviews.

In some embodiments, regarding Peer Recommendations qualified by PII,the identity of the recommender may be anonymous unless the recommenderchooses otherwise. The YP platform may provide the ability for the userto define when the identity of the recommender may be anonymous or not.Peer-to-peer (P2P) recommendations may be one of the biggest influencersin a user (consumer) making a product decision. Many products where“personalization” may be a new elective feature, in financial products,because of risk profiling, financial parameters and risk. One of thegoals of the providers of consumer finance may be that the basic productitself needs to fit a specific consumer profile. The financial profilesof different users may be very different, which may create differentrisk profiles for a provider of consumer finance. For example, withregard to certain types of loans (e.g. mortgages), if the financialprofile of user1 is much different than that of user 2, the product (aHome Equity Line of Credit Mortgage) that user1 was offered and possiblyreceived may not be available to user2. However, the amount of detailand complex calculations needed to make these assessments are notusually practical (due to knowledge of the calculations and theweighting of importance of the factors), nor possible (due to user1'sdesire for privacy and/or information that will not be shared withuser2). Due to this, the power of Peer-to-Peer recommendations maybecome much more limited in financially qualified products, since theability for the consumer to know the recommender's financial situationis relative to their own.

In some embodiments, third party providers (e.g., market place) may alsohave accounts for using the YP platform. The third-party providers whohave an account on the YP platform may include but is not limited to:

(1) Budget Counseling NGOs

(2) Budget Counseling online services (APPS)

(3) CPA's

(4) Wealth Management providers(5) Wealth Management online services (APPS)(6) Government needs-based programs

(7) Financial Literacy NGOs

(8) Home Ownership readiness program providers(9) Credit repositories (Experian, Transunion and Equifax)(10) Mortgage lenders

(11) Loan Origination Software Publishers (Elli Mae)

(12) Banks (from community to Large multinational)

(13) Credit Unions

(14) Independent developers of software (APPS) that do any of the above,or any combination thereof

(15) Data Aggregators

(16) Non-financial provider advertising

In some embodiments, some of the entities above may use an API(Application programming interface) that will integrate with theirEnterprise software system with the YP platform. For example, in certaincases the Publisher, such as a loan origination software publisher, mayhave the API integration built-in for any lender using the software, whois also a member of the system managing the YP platform. Providers maycreate parameter factors and weight them (e.g. prioritize them) with theGeneric PII MAP tool.

In some embodiments, other functionality may include autonomous matchingof the synthetic data of the user 10. This may be done on two levels inthe YP system master guide (e.g., autonomous software agent) andindividually with the user 10 if they may have allowed that function forthe provider type.

In some embodiments, the market place may include a credit provider thatmay define Profiles, elements, targets etc. Messaging may be seen beforethe consumer has opted in. Incentives may be provided to consumers.Autonomous agent matching caudate profiles.

For example, the Autonomous Agent may identify a consumer with a profilesought by ABC bank and XYZ Credit card company, who are willing to give$50 (or a toaster) to the consumer for signing up. Assuming theparticular consumer has not already permissioned getting such offers,the YP system may display a message to the consumer utilizing theoptimal UIX that “if you want to opt in now, there are two companieswilling to give you a $50 (or a toaster) for signing up”, for example.

In some embodiments, opt-in levels may be pull vs push, such asthird-party apps receiving data from YP platform more efficiently thanthe user inputting and providing the data directly. The user mayultimately have the control that no company will automatically get PIIdata other than anonymized data with hash general setting.

In some embodiments, the Market Place Providers Platform may have creditproviders defining profiles, elements, targets etc. Messaging may beseen before the consumer has opted in. Incentives may be provided to theconsumer. Autonomous agent matching caudate profiles, Opt in levels, andAPI integration with credit providers account origination software.

In some embodiments, while education may be provided mainly throughproviders in the market place, the system may provide tools to qualifieddevelopers, utilize the same elements described in the optimal UIX(Psychographics etc. etc.) to help optimize motivation, attention span,and cognitive enjoyment of the user.

In some embodiments, the YP platform is a tool for the user on anongoing basis to understand, to start any journey they choose, and endup at any of these companies better prepared and easier to work with.Today to fill out a loan application, the user works hard, and may notunderstand the entire flow. The YP platform may become part of theconsumer routine where the user is more cognitive and educated abouttheir financial choices while retaining more control and transparency.

In some embodiments, due to the economic/market limitations that may becaused by imperfect and asymmetric information, it may not be possibleto efficiently address the inefficiencies of imperfect and asymmetricinformation comprehensively. The YP platform creates the possibility toexample these limitations while maintaining privacy and providing adepth of knowledge in the weighting and calculations. The Peer-to-Peerrecommendations may be done with user1 utilizing know how and user2 PPIeven though user1 does not possess either one.

In some embodiments, the YP platform may enable the consumer to receiveremuneration from the YP platform or third parties for the use of theuser's PII.

The embodiments in this disclosure herein disclose for a specified useror consumer, the steps of:

-   -   1. Opening an account with field inputs or scanning IDs    -   2. A login portal allowing the user to log in to their various        online accounts    -   3. The YourPii application may allow the user to aggregate their        own data from online accounts:        -   a. The client may log into their bank account From within            the YP Application (through the use of a vendor such as            Akoya, for example) and may (options) download PDF            statements, Screen scrape account information from browser,            and/or download data.        -   b. With the user's authorization, multiple possible data            sources may be identified through the YourPII app personal            profile crawler that utilizes the AI/algorithm/rules module            43 in the PI Print application to identify sources such as a            bank credit card on the credit report, or the interest            reported on a 1099 form.    -   4. The YourPii application extracts (if needed), analyzes the        data, and classifies that data. The data may then be encrypted        -   a. A bank account statement may be identified as high            descriptive sensitive financial data and classified as based            on a personal economic data structure value class (see            below)    -   5. The YourPii application may stores data in separate secure        containers based on data classification.    -   6. The YourPii's PII Print application using the        AI/algorithm/rules module 43 may extract data (if needed such as        extracting a DOB from an image of a Driver's license) and        analyzes the data based on demographic, behavioral,        psychometric, psychographic factors as well as other methods        used for human profiling, hereinafter referred to as Life Path        Trend Profile (LPTP).    -   7. The YourPii's PII Print [Personal Economics (PE)] application        using the AI/algorithm/rules module 43 may extract data (if        needed such as extracting account balances from a Bank Statement        downloaded as a PDF), analyzes the data, and tags the data such        with data types such as income, debt, payment obligations,        liquid assets, non-liquid assets, direct liabilities, contingent        liabilities, and other data relating directly or indirectly to        financial factors. Tags may be metadata, or a data value        expressed in a particular data standard, predefined data schema,        or syntax. The metadata, data standard, schema, or syntax may        include at least one standard from industry, NGO (organizations        or open source) and/or Government agencies, including Uniform        Loan Application Dataset (ULAD), Uniform Residential Loan        Application (URLA), The Mortgage Industry Standards Maintenance        Organization (MISMO), eXtensible Business Reporting Language        (XBRL), Object Management Group® (OMG®) and the like.    -   8. The YourPii's PII Print application using the        AI/algorithm/rules module 43 based on steps 6 and 7 hereinabove        may generate two and/or three vectors in the data structure.        Each YourPii PII Print application may create its own data        structure such as one where the data points are exclusively PE        factors and another one where the data points are exclusively        LPTP factors. Each point in these data structures may represent        a unique value including:        -   a. An element value (e.g. date of birth DOB)        -   b. How the values of two elements are related and expressed            mathematically. For example, it may be the sum of one            element value divided by another or another factor such as            annual debt payments divided by annual income, for example.        -   c. The data source and/or type of a particular elemental            value (e.g. DOB retrieved from Driver's License (DL))        -   d. The date of the relevant data source and/or type of a            particular elemental value (e.g., a valid DL or if an            elemental data value is dynamic, e.g. a bank account balance            that may change periodically with the month and day of the            statement).        -   e. A binary factor—even if a data structure element or point            (e.g. “d” above) may still be valid, such as an 8-month-old            bank statement. The 8-month-old bank statement may not be            relevant by itself. However, a current statement with two            previous statements may show a trend, whereas the            8-month-old bank statement may not be valid on a standalone            basis.        -   f. A score value rating indicative of the data source            validating factor value (e.g. “d” above). The data source            with a lower score may be assigned to a copy of a paycheck            as a validation for the source of income. However, income            data retrieved from the IRS website or another trusted third            party may have a higher score value.        -   g. Demographic elemental values such as Gender and/or in “c”            above        -   h. Demographic values relating to the current quality, age,            or presence in the data structure elemental values (like “d”            above) which is true for all elemental data types.        -   i. Psychographic elemental values based on social issues            that the individual may have expressed interest, based on            what was last retrieved from Social Media (step 108) and/or            spending habits (step 112) based on analysis of credit card            or bank account statements.        -   j. Psychographic values related to the current quality, age,            or presence in the data structure elemental values (like “d”            above)        -   k. Behavioral elemental values (based on user's usage            history of the method, or retrieved from Social Media, data            from analysis of credit card or bank account statements,            responses to requests for direct user input (step 116),            which may be binary, multiple choice or direct field input            from the GUI 70.        -   l. Behavioral values relating to the current quality, age,            or presence in the data structure elemental values (like “d”            above).    -   9. The YourPii application may design an optimal UIX for the        user, based on the input of certain data structure values into        the Your PII UI engine. The analysis may include the following:        -   a. Demographic factors        -   b. Psychographic factors        -   c. Behavioral factors        -   d. Current best practices in UI design including            -   i. Decision Fatigue            -   ii. Other cognitive psychology factors that comes in                effect with the field of interface design.    -   10. Based on the results (output) of the Your PII UI engine in        step S, the YourPii application may choose from a library of UI        themes and graphical elements.        -   a. These themes and graphical elements may be part of the YP            generated library or generated by third parties. This may            include elements from well-known designers at a cost if the            user chooses.    -   11. Based on the results (output) of the Your PII UI engine in        steps 8 & 9, the YourPii application may provide the user a        series of a binary choices to refine, and to check the user        interface and experience definitions    -   12. Based on the User choices (which may include the ability to        visualize or try various UI configurations), a UI configuration        may be generated and presented as the UI of the Users YourPII        app.    -   13. The user may be able to redo the process laid out above.    -   14. The Your PII UI engine using the AI/algorithm/rules module        43 may use each set of results to help train the AI engine        (e.g., machine learning models) and may ask the user if the user        is willing to become an active participant in a test group        [e.g., to define community development design group]    -   15. User interface and experience may allow the user to skip        step 11 (binary) and allow the YourPII platform to just create a        UI.    -   16. Step 11 may iterate the UIX based on parameters determined        in steps 6-8.    -   17. The YourPii application may analyze what data is missing        (e.g., gap analysis) from the PII Print.        -   a. The YP platform may continuously identify missing data            and/or values within the user's various data structures.        -   b. These missing values may be prioritized by various values            that may be dynamic, including the intent of the user            whether that has been stated or has been derived from the            AI/algorithm/rules module 43.    -   18. The YP platform may guide the user (depending on the UIX        defined in the previous processes) which may include explaining        what factors are missing, why the particular factors are        important, and how to add them to their YP Vault, which may        include inputting data through the personalized UI (icons,        dropdowns, field fill, and/or through any other graphical        elements), from a local drive or device, a third party site, as        well as other sources.

II. The uses of the herein disclosed embodiments may include:

-   -   a. (Consume/User)        -   Opting in to a credit provider request        -   Finding a provider of services in the market place and            engaging them        -   Friend recommendations        -   Matched User Reviews    -   b. Credit Provider        -   Provider defines        -   Profiles elements targets etc.        -   Messaging seen before consumer has opted in        -   Incentives to consumer        -   Autonomous agent matching caudate profiles        -   Opt in levels        -   API integration with Credit providers account origination            software    -   c. Provider (products and services other than credit)

FIG. 7 is a flowchart of a method 250 for self-aggregation of personaldata and personal data custody, control, and stewardship in accordancewith one or more embodiments of the present disclosure. Method 250 mayby performed by the logic layer processor 40 of the server 15.

Method 250 may include receiving 252, by a logic layer processor, over acommunication network, from a plurality of electronic resources, initialuser personal identifiable information (PII) of a user of a plurality ofusers, where user PII includes a plurality of data elements.

Method 250 may include classifying 254 the plurality of data elements ofthe initial PII of the user to populate a profile map data structurehaving a standardized predefined data schema of a plurality of vectorelements so as to form a user-specific profile map data structure of theuser, including at least a plurality of: (i) a demographic user-specificparameter, (ii) a psychographic user-specific parameter, (iii) abehavioral user-specific parameter, (iv) a quantitative user-specificparameter, or (v) any combination thereof.

Method 250 may include iteratively receiving 256 over the communicationnetwork, from the plurality of electronic resources, additional userpersonal identifiable information (PII) of the user based at least inpart on the user-specific profile map data structure.

Method 250 may include iteratively classifying 258 the additional userPII of the user to update the user-specific profile map data structureof the user.

Method 250 may include enabling 260 a plurality of user-specific datamanagement software functions based on the user-specific profile mapdata structure.

In some embodiments, the term transformed user-specific PII may refer tosynthetic data, anonymized data, Readable PII, Metadata, or anycombination thereof.

FIG. 8 depicts PIIMAP Layers 265 in accordance with one or moreembodiments of the present disclosure. The PIIMap Layers may include butare not limited to Metadata 1—third party verification 279, ademographic profile 267 and metadata 2 269, a psychographic profile 271and metadata 3 273, a behavioral profile 272 and metadata 4 274 andcustomizable dynamic profile based on financial ratios 275 and metadataN 277. Source files 291 may include data that the user may upload 281from a local drive and/or data that the user imports 283 from onlineaccounts. The data that the user imports from online accounts 283 and/orpermissioned third-party contributions 284 may be original data importswith source certification 289. User inputs 285 may include originaluser's data inputs and usage data 287.

FIG. 9 depicts application programming interface (API) and Data Schemacompatibilities 295 in accordance with one or more embodiments of thepresent disclosure. The YP platform 80 may allow data imports and/ordata exports with a plurality of API/Data Schema compatibilities whichmay include Data Aggregation Industry 297 (schema from Akoya company,FDX, and OFX), Modernized e-file System (MeF) 299, GSE/Mortgage Industry301 (URLA, ULDA, and ILDA), Asset and income Modeler (AIM) 303 such asFreddie Mac, social media industry 305 such as DTP and Takeout, Metro2307 (Consumer Credit Reporting Industry).

FIG. 10 depicts a third exemplary screenshot 330 of the GUI 70 inaccordance with one or more embodiments of the present disclosure. Thethird exemplary screenshot 330 may be displayed on the GUI 70 on thedisplay 76A for allowing the user 10 interacting with PIIMAP 5 via UIXlogic 331 with graphical elements such as for example, health records332 of the user 10, browsing history 333 of the user 10, gaming history334 of the user 10, and banking 335 of the user 10. The third exemplaryscreenshot 330 is an example of an optimal UIX generated specificallyfor the user 10 based on his PIIMAP.

The embodiments of the method and system for self-aggregation ofpersonal data and personal data custody, control, and stewardship asdisclosed hereinabove are not limited to financial use cases, but mayalso be applied to digital advertising. Consumers (users) may preferreceiving targeted content and advertisements from entities in theadvertising industry. Conversely, these entities need the consumer'sbehavioral and/or demographic personal data to be able to targetadvertisements and/or user-targeted content. However, data privacyrights and/or new data privacy regulations may prevent the collection ofthis personal data of the consumer without consent. Typically, theseentities have obtained this behavioral and/or demographic personal dataof the consumer through cookie-tracking based on websites previouslyvisited by the consumer. Regulatory requirements and consumer awarenessof privacy right are changing the landscape quickly for these entitiesby phasing out third-party cookies to first-party data thus decreasingreliance on cookies. The YP platform may be in place of, or maysignificantly enhance cookies, identifiers and other methods used byonline advertisers.

FIG. 11 depicts an exemplary flow diagram 340 in accordance with one ormore embodiments of the present disclosure. Normally digital advertisingproviders like Taboola 342 and Outbrain 345, for example, may trackcookies 343 on the user's computing device 65. However, in someembodiments, Taboola 342 and Outbrain 345 may request 347 PII data fromthe YI platform 80 in order to tailor and provide user-specificadvertisements to these digital advertising providers. In response tothe request 347, the required PII data 349 may be sent to theseproviders for ad retargeting.

In some embodiments, the YP platform 80 for self-aggregation of personaldata and and personal data custody, control, and stewardship asdisclosed herein may be used to:

-   -   (1) To educate the user of the value of their PII that is shared        with advertisers,    -   (2) To educate the user as to what data the advertising industry        needs and why,    -   (3) To designate a portion of the user's PII in the PII MAP 5        with proper assess rights that may be shared with these entities        of the advertising industry in real time,    -   (4) To provide an anonymized portion of the PII data available        for targeting and personalized advertising content from third        party providers of advertising providers.

The embodiments of the method and system for self-aggregation ofpersonal data and personal data custody, control, and stewardship asdisclosed hereinabove are not limited to financial use cases, but mayalso be applied to legal services profiling and matching. The YPplatform for this use case may be used to help the consumer who needs torepresent themselves, identify the set of available “Access to Justice”(A2J) resources that best match their particular needs using the PII MAP5 as previously described as follows:

STEP 1: Intake Interview

In some embodiments, an online interview driven by artificialintelligence (AI) may mimic the personalized question and answer (Q&A)flow of a human A2J resource interview. The self-represent litigant(SRL) may be guided via GUI 70 to fill in information based on theirspecific situation. The information/documentation may be retained, sothe Consumer SRL (user) does not need to repeat the same steps as theymove to different support providers. The user retains secure control ofthe user's personal data. The user's consent may be needed when theywish to share their information with specific resource organizationsand/or professionals.

STEP 2: Profile Generation

In some embodiments, the interview data may be analyzed by YP platformwith the AI/algorithm/rules module 43 with PII MAP models and YourPIIRuntime application. The algorithms may automatically and quickly createan individualized profile for the SRL [e.g. based on the PII MAPneeds-based services and county regulations that can be personalized].Each unique profile may include key demographic and historical data. Thematching engine may recommend suitable prioritized resources.

STEP 3: Matching Basic Parameters of the Consumer to Resources

In some embodiments, based on the PIIMAP 5 and the legal parameter data,the matching engine may apply artificial intelligence algorithms tomatch the case type and categorical profile factors of the A2J consumerand constituency profiles and other parameters of the A2J resources inthe system.

STEP 4: Resource Availability Estimation and Permissioned Delivery

In some embodiments, resources may establish preferred SRL profiles andmay prioritize each consumer SRL profiles against it or dynamic factorssuch as their workload resources available. The resources may authorizesystem default or provide estimated timeframes of their availability.Through the YP platform. the Consumer SRL (user) may determine the levelof information to share with a particular A2J resource based on theconsumer's preference and the range of information that the A2J resourcehas stated that they need and explained why. The consumer may beprompted on the GUI 70 as to what are the possible ramifications ofsharing or not sharing certain data.

STEP 5: Matching of Consumer SRL to Resources with Detailed ConsumerInformation

In some embodiments, the matching engine may use artificial intelligencealgorithms to identify the most appropriate resources based onPermissioned Detailed Consumer SRL personal file (e.g., PIIMAP 5 withLitigation Permissioned data including uploaded files). In otherembodiments, the matching engine may use artificial intelligencealgorithms to identify the most appropriate resources based on case typeand categorical profile factors, and to delivers upload/imported filesto the resource.

STEP 6: Consumer Dashboard with Potential Resources Displayed to theUser

FIG. 12 depicts a fourth exemplary screenshot 350 of the GUI 70 inaccordance with one or more embodiments of the present disclosure.Potential resources may be displayed to the user 10 such as for example,HelpforYou.org 352, Pro-bono Esq 354, Legal Assistance your County 356,We've been There Network 358, and MicroDonors $$ For You 360.

In some embodiments, based on results and responses, the system maysequence the resources and their availability into a workflow, and maycreate an optimized plan of resources and possible interactions. Theuser 10 (e.g., the consumer) may be shown potential available resourcesin a dashboard format on the GUI 70, so the user 10 may understand whothe resource is and how that resource may be help the user. The consumermay be shown how resources may be able to work together and potentialnext steps. The consumer may choose whom the user wants to use and toreceive services from.

STEP 7: Consumer SRL On-Boarding and Hand Off to Resource

In some embodiments, each resource may specify their own next stepprocedure. The consumer's SRL file may be safely stored in the systemavailable only to the chosen resources and based on the resources theconsumer has grant access right permissions. At this point, the hand offbetween the consumer and the chosen resource is complete. The consumer'sSRL file may stay with the resource for a predetermined period of timeunless the consumer extends the time period. The consumer SRLinteractions are directly with the resources that they have chosen.Depending on the level of data sharing previously granted permissionrights by the consumer, the consumer may choose to share moreinformation with the chosen A2J resource(s) so as to enable the resourceto start their onboarding process.

FIG. 13 depicts an exemplary flow diagram 370 for a second use case inaccordance with one or more embodiments of the present disclosure. Thesecond use case shown in FIG. 13 is generates a game design and logicUIX 382 for gamers that may be displayed on the GUI 70 on the computingdevice 65 of the user 10 using the function for generating agamification UIX 176 as shown in FIG. 5.

In some embodiments, the PIIMAP 5 may be used by the function forgenerating a gamification UIX 176 through a plurality of game factors,which may be game design elements or logic elements denoted as GameFactor1 372, Game Factor2 374, Game Factor3 376, Game Factor4 378, andGame Factor5 380. These may be attributes (action level and/or theories)from the YP platform or from third party computing devices. The YP mayprovide access to the profiling including demographic, psychographic,cognitive, behavioral, and quantitative factors, and game play history.The logic and design elements interacting with the player PIIMAP maycome from the YP platform or a third party.

In some embodiments, the function for generating a gamification UIX 176may generate an output that customizes the game experience for the user10 on any game platform such as on a personal computer (PC) or mobiledevice. In addition to the game design and logic UIX 382 displayed onthe GUI 70 on the computing device 65 of the user 10, the customizedgame experience may be outputted to a plurality of gaming platforms suchas Series XS 384, Playstation 386, Unity 388, Steam 390, and PC game392, for example.

FIG. 14 depicts an exemplary flow diagram 600 for a third use case inaccordance with one or more embodiments of the present disclosure. Thethird use case shown in FIG. 14 is generates a gamification UIX 630 thatmay be displayed on the GUI 70 on the display 76A on the computingdevice 65 of the user 10 using the function for generating agamification UIX 176 as shown in FIG. 5.

In some embodiments, the PIIMAP 5 may be used by the function forgenerating a gamification UIX 176 through a plurality of game factors,which may be game design elements or logic elements denoted as GameFactor1 372, Game Factor2 374, Game Factor3 376, Game Factor4 378, andGame Factor5 380. These may be attributes (action level and/or theories)from the YP platform or from third party computing devices. The functionfor generating a gamification UIX 176 may generate an output that goesto a UIX Logic Controller 610 that creates the unified gamifiedexperience. The gamification UIX 630 may include generated gamificationgraphical elements 635 such as “Web History”, “Bank Statements”, “For MyCPA”, “Financial Planner”, “Prescriptions”, “Medical Records”, “CreditReport and Score”, “Mortgage Lenders”, “Gaming History”, “PurchaseHistory”, “Household Budgeting”, “Benefits”, and “Tips and Guides”.Additional graphical elements may include “My Teams Score” 636, “MyCredits Earned” 637, “My Better Habits” 638, “My Progress” 639, “MyCoach” 640, and “My Team” 641.

In some embodiments, exemplary inventive, specially programmed computingsystems/platforms with associated devices are configured to operate inthe distributed network environment, communicating with one another overone or more suitable data communication networks (e.g., the Internet,satellite, etc.) and utilizing one or more suitable data communicationprotocols/modes such as, without limitation, IPX/SPX, X.25, AX.25,AppleTalk™, TCP/IP (e.g., HTTP), near-field wireless communication(NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitablecommunication modes. In some embodiments, the NFC can represent ashort-range wireless communications technology in which NFC-enableddevices are “swiped,” “bumped,” “tap” or otherwise moved in closeproximity to communicate. In some embodiments, the NFC could include aset of short-range wireless technologies, typically requiring a distanceof 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHzon ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to424 kbit/s. In some embodiments, the NFC can involve an initiator and atarget; the initiator actively generates an RF field that can power apassive target. In some embodiments, this can enable NFC targets to takevery simple form factors such as tags, stickers, key fobs, or cards thatdo not require batteries. In some embodiments, the NFC's peer-to-peercommunication can be conducted when a plurality of NFC-enable devices(e.g., smartphones) within close proximity of each other.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.),serverless distributed, erasure coding storage, and others.

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth

Computer-related systems, computer systems, and systems, as used herein,include any combination of hardware and software. Examples of softwaremay include software components, operating system software, middleware,firmware, software modules, routines, subroutines, functions, methods,procedures, software interfaces, application program interfaces (API),instruction sets, computer code, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay include or be incorporated, partially or entirely into at least onepersonal computer (PC), laptop computer, ultra-laptop computer, tablet,touch pad, portable computer, handheld computer, palmtop computer,personal digital assistant (PDA), cellular telephone, combinationcellular telephone/PDA, television, smart device (e.g., smart phone,smart tablet or smart television), mobile internet device (MID),messaging device, data communication device, Alexa, smart wearabledevices, and so forth.

As used herein, the term “server” should be understood to refer to aservice point which provides processing, database, and communicationfacilities. By way of example, and not limitation, the term “server” canrefer to a single, physical processor with associated communications anddata storage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud servers are examples. This may include any networkarchitecture such as server less architecture, peer-to-peer, and/ordistributed computing architectures.

In some embodiments, as detailed herein, one or more of exemplaryinventive computer-based systems/platforms, exemplary inventivecomputer-based devices, and/or exemplary inventive computer-basedcomponents of the present disclosure may obtain, manipulate, transfer,store, transform, generate, and/or output any digital object and/or dataunit (e.g., from inside and/or outside of a particular application) thatcan be in any suitable form such as, without limitation, a file, acontact, a task, an email, a social media post, a map, an entireapplication (e.g., a calculator), etc. In some embodiments, as detailedherein, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be implemented across one or more of various computer platforms suchas, but not limited to: (1) FreeBSD, NetBSD, OpenBSD; (2) Linux; (3)Microsoft Windows; (4) OS X (MacOS); (5) MacOS 11; (6) Solaris; (7)Android; (8) iOS; (9) Embedded Linux; (10) Tizen; (11) WebOS; (12) IBMi; (13) IBM AIX; (14) Binary Runtime Environment for Wireless (BREW);(15) Cocoa (API); (16) Cocoa Touch; (17) Java Platforms; (18) JavaFX;(19) JavaFX Mobile; (20) Microsoft DirectX; (21) .NET Framework; (22)Silverlight; (23) Open Web Platform; (24) Oracle Database; (25) Qt; (26)Eclipse Rich Client Platform; (27) SAP NetWeaver; (28) Smartface; and/or(29) Windows Runtime.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to utilize hardwired circuitry that may be used inplace of or in combination with software instructions to implementfeatures consistent with principles of the disclosure. Thus,implementations consistent with principles of the disclosure are notlimited to any specific combination of hardware circuitry and software.

For example, various embodiments may be embodied in many different waysas a software component such as, without limitation, a stand-alonesoftware package, a combination of software packages, or it may be asoftware package incorporated as a “tool” in a larger software product.

For example, exemplary software specifically programmed in accordancewith one or more principles of the present disclosure may bedownloadable from a network, for example, a website, as a stand-aloneproduct or as an add-in package for installation in an existing softwareapplication. For example, exemplary software specifically programmed inaccordance with one or more principles of the present disclosure mayalso be available as a client-server software application, or as aweb-enabled software application. For example, exemplary softwarespecifically programmed in accordance with one or more principles of thepresent disclosure may also be embodied as a software package installedon a hardware device.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to handle numerous concurrent users that may be, butis not limited to, at least 100 (e.g., but not limited to, 100-999), atleast 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000(e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., butnot limited to, 100,000-999,999), at least 1,000,000 (e.g., but notlimited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but notlimited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but notlimited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., butnot limited to, 1,000,000,000-999,999,999,999), and so on.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to output to distinct, specifically programmedgraphical user interface implementations of the present disclosure(e.g., a desktop, a web app., etc.). In various implementations of thepresent disclosure, a final output may be displayed on a displayingscreen which may be, without limitation, a screen of a computer, ascreen of a mobile device, or the like. In various implementations, thedisplay may be a holographic display. In various implementations, thedisplay may be a transparent surface that may receive a visualprojection. Such projections may convey various forms of information,images, and/or objects. For example, such projections may be a visualoverlay for a mobile augmented reality (MAR) application.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to be utilized in various applications which mayinclude, but not limited to, gaming, mobile-device games, video chats,video conferences, live video streaming, video streaming and/oraugmented reality applications, mobile-device messenger applications,and others similarly suitable computer-device applications.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, Smartphone,tablets, or any other reasonable mobile electronic device.

As used herein, the terms “proximity detection,” “locating,” “locationdata,” “location information,” and “location tracking” refer to any formof location tracking technology or locating method that can be used toprovide a location of, for example, a particular computingdevice/system/platform of the present disclosure and/or any associatedcomputing devices, based at least in part on one or more of thefollowing techniques/devices, without limitation: accelerometer(s),gyroscope(s), Global Positioning Systems (GPS); GPS accessed usingBluetooth™; GPS accessed using any reasonable form of wireless and/ornon-wireless communication; WiFi™ server location data; Bluetooth™ basedlocation data; triangulation such as, but not limited to, network basedtriangulation, WiFi™ server information based triangulation, Bluetooth™server information based triangulation; Cell Identification basedtriangulation, Enhanced Cell Identification based triangulation,Uplink-Time difference of arrival (U-TDOA) based triangulation, Time ofarrival (TOA) based triangulation, Angle of arrival (AOA) basedtriangulation; techniques and systems using a geographic coordinatesystem such as, but not limited to, longitudinal and latitudinal based,geodesic height based, Cartesian coordinates based; Radio FrequencyIdentification such as, but not limited to, Long range RFID, Short rangeRFID; using any form of RFID tag such as, but not limited to active RFIDtags, passive RFID tags, battery assisted passive RFID tags; or anyother reasonable way to determine location. For ease, at times the abovevariations are not listed or are only partially listed; this is in noway meant to be a limitation.

As used herein, the terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to securely store and/or transmit data byutilizing one or more of encryption techniques (e.g., private/public keypair, Triple Data Encryption Standard (3DES), block cipher algorithms(e.g., IDEA, RC2, RCS, CAST and Skipjack), cryptographic hash algorithms(e.g., MDS, RIPEMD-160, RTRO, SHA-1, SHA-2, Tiger (TTH),WHIRLPOOL,RNGs). The aforementioned examples are, of course, illustrative and notrestrictive.

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber” “consumer” or“customer” should be understood to refer to a user of an application orapplications as described herein, and/or a consumer of data supplied bya data provider. By way of example, and not limitation, the terms “user”or “subscriber” can refer to a person who receives data provided by thedata or service provider over the Internet in a browser session or canrefer to an automated software application which receives the data andstores or processes the data. There may be two categories of users: (1)the consumer user who aggregates, manages and uses their PII data, and(2) the provider user who is providing goods and services to theconsumer based in part on the consumer's PII. The provider user may bein the marketplace where they may be registered and fully integratedwith the YP platform, or those provider users that may purchase leadgeneration services through the YP platform.

FIG. 15 depicts a block diagram of an exemplary computer-basedsystem/platform 400 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the exemplary inventive computing devices and/or theexemplary inventive computing components of the exemplary computer-basedsystem/platform 400 may be configured to manage a large number ofmembers and/or concurrent transactions, as detailed herein. In someembodiments, the exemplary computer-based system/platform 400 may bebased on a scalable computer and/or network architecture thatincorporates varies strategies for accessing the data, caching,searching, and/or database connection pooling. An example of thescalable architecture is an architecture that is capable of operatingmultiple servers.

In some embodiments, referring to FIG. 13, members 402-404 (e.g.,clients) of the exemplary computer-based system/platform 400 may includevirtually any computing device capable of receiving and sending amessage over a network (e.g., cloud network), such as network 405, toand from another computing device, such as servers 406 and 407, eachother, and the like. In some embodiments, the member devices 402-404 maybe personal computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, and the like. In someembodiments, one or more member devices within member devices 402-404may include computing devices that typically connect using a wirelesscommunications medium such as cell phones, smart phones, pagers, walkietalkies, radio frequency (RF) devices, infrared (IR) devices, CBs,integrated devices combining one or more of the preceding devices, orvirtually any mobile computing device, and the like. In someembodiments, one or more member devices within member devices 402-404may be devices that are capable of connecting using a wired or wirelesscommunication medium such as a PDA, POCKET PC, wearable computer, alaptop, tablet, desktop computer, a netbook, a video game device, apager, a smart phone, an ultra-mobile personal computer (UMPC), and/orany other device that is equipped to communicate over a wired and/orwireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, etc.). In some embodiments,one or more member devices within member devices 402-404 may include mayrun one or more applications, such as Internet browsers, mobileapplications, voice calls, video games, videoconferencing, and email,among others. In some embodiments, one or more member devices withinmember devices 402-404 may be configured to receive and to send webpages, and the like. In some embodiments, an exemplary specificallyprogrammed browser application of the present disclosure may beconfigured to receive and display graphics, text, multimedia, and thelike, employing virtually any web based language, including, but notlimited to Standard Generalized Markup Language (SMGL), such asHyperText Markup Language (HTML), a wireless application protocol (WAP),a Handheld Device Markup Language (HDML), such as Wireless MarkupLanguage (WML), WMLScript, XML, JavaScript, and the like. In someembodiments, a member device within member devices 402-404 may bespecifically programmed by either Java, .Net, QT, C, C++ and/or othersuitable programming language. In some embodiments, one or more memberdevices within member devices 402-404 may be specifically programmedinclude or execute an application to perform a variety of possibletasks, such as, without limitation, messaging functionality, browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded messages, images and/or video,and/or games.

In some embodiments, the exemplary network 405 may provide networkaccess, data transport and/or other services to any computing devicecoupled to it. In some embodiments, the exemplary network 405 mayinclude and implement at least one specialized network architecture thatmay be based at least in part on one or more standards set by, forexample, without limitation, Global System for Mobile communication(GSM) Association, the Internet Engineering Task Force (IETF), and theWorldwide Interoperability for Microwave Access (WiMAX) forum. In someembodiments, the exemplary network 405 may implement one or more of aGSM architecture, a General Packet Radio Service (GPRS) architecture, aUniversal Mobile Telecommunications System (UMTS) architecture, and anevolution of UMTS referred to as Long Term Evolution (LTE). In someembodiments, the exemplary network 405 may include and implement, as analternative or in conjunction with one or more of the above, a WiMAXarchitecture defined by the WiMAX forum. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary network 405 may also include, for instance, at least oneof a local area network (LAN), a wide area network (WAN), the Internet,a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual privatenetwork (VPN), an enterprise IP network, or any combination thereof. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, at least one computer network communicationover the exemplary network 405 may be transmitted based at least in parton one of more communication modes such as but not limited to: NFC,RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite and any combination thereof. In someembodiments, the exemplary network 405 may also include mass storage,such as network attached storage (NAS), a storage area network (SAN), acontent delivery network (CDN) or other forms of computer or machinereadable media.

In some embodiments, the exemplary server 406 or the exemplary server407 may be a web server (or a series of servers) running a networkoperating system, examples of which may include but are not limited toMicrosoft Windows Server, Novell NetWare, or Linux. In some embodiments,the exemplary server 406 or the exemplary server 407 may be used forand/or provide cloud and/or network computing. Although not shown inFIG. 15, in some embodiments, the exemplary server 406 or the exemplaryserver 407 may have connections to external systems like email, SMSmessaging, text messaging, ad content providers, etc. Any of thefeatures of the exemplary server 406 may be also implemented in theexemplary server 407 and vice versa.

In some embodiments, one or more of the exemplary servers 406 and 407may be specifically programmed to perform, in non-limiting example, asauthentication servers, search servers, email servers, social networkingservices servers, SMS servers, IM servers, MMS servers, exchangeservers, photo-sharing services servers, advertisement providingservers, financial/banking-related services servers, travel servicesservers, or any similarly suitable service-base servers for users of themember computing devices 401-404.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for example, one or more exemplary computingmember devices 402-404, the exemplary server 406, and/or the exemplaryserver 407 may include a specifically programmed software module thatmay be configured to send, process, and receive information using ascripting language, a remote procedure call, an email, a tweet, ShortMessage Service (SMS), Multimedia Message Service (MMS), instantmessaging (IM), internet relay chat (IRC), mIRC, Jabber, an applicationprogramming interface, Simple Object Access Protocol (SOAP) methods,Common Object Request Broker Architecture (CORBA), HTTP (HypertextTransfer Protocol), REST (Representational State Transfer), or anycombination thereof.

FIG. 16 depicts a block diagram of another exemplary computer-basedsystem/platform 500 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the member computing devices 502 a, 502 b thru 502 n showneach at least includes a computer-readable medium, such as arandom-access memory (RAM) 508 coupled to a processor 510 or FLASHmemory. In some embodiments, the processor 510 may executecomputer-executable program instructions stored in memory 508. In someembodiments, the processor 510 may include a microprocessor, an ASIC,and/or a state machine. In some embodiments, the processor 510 mayinclude, or may be in communication with, media, for examplecomputer-readable media, which stores instructions that, when executedby the processor 510, may cause the processor 510 to perform one or moresteps described herein. In some embodiments, examples ofcomputer-readable media may include, but are not limited to, anelectronic, optical, magnetic, or other storage or transmission devicecapable of providing a processor, such as the processor 510 of client502 a, with computer-readable instructions. In some embodiments, otherexamples of suitable media may include, but are not limited to, a floppydisk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, aconfigured processor, all optical media, all magnetic tape or othermagnetic media, or any other medium from which a computer processor canread instructions. Also, various other forms of computer-readable mediamay transmit or carry instructions to a computer, including a router,private or public network, or other transmission device or channel, bothwired and wireless. In some embodiments, the instructions may includecode from any computer-programming language, including, for example, C,C++, Visual Basic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, member computing devices 502 a through 502 n mayalso include a number of external or internal devices such as a mouse, aCD-ROM, DVD, a physical or virtual keyboard, a display, a speaker, orother input or output devices. In some embodiments, examples of membercomputing devices 502 a through 502 n (e.g., clients) may be any type ofprocessor-based platforms that are connected to a network 506 such as,without limitation, personal computers, digital assistants, personaldigital assistants, smart phones, pagers, digital tablets, laptopcomputers, Internet appliances, and other processor-based devices. Insome embodiments, member computing devices 502 a through 502 n may bespecifically programmed with one or more application programs inaccordance with one or more principles/methodologies detailed herein. Insome embodiments, member computing devices 502 a through 502 n mayoperate on any operating system capable of supporting a browser orbrowser-enabled application, such as Microsoft™, Windows™, and/or Linux.In some embodiments, member computing devices 502 a through 502 n shownmay include, for example, personal computers executing a browserapplication program such as Microsoft Corporation's Internet Explorer™,Apple Computer, Inc.'s Safari™, Mozilla Firefox, and/or Opera. In someembodiments, through the member computing client devices 502 a through502 n, users, 512 a through 512 n, may communicate over the exemplarynetwork 506 with each other and/or with other systems and/or devicescoupled to the network 506. As shown in FIG. 16, exemplary serverdevices 504 and 513 may be also coupled to the network 506. In someembodiments, one or more member computing devices 502 a through 502 nmay be mobile clients.

In some embodiments, at least one database of exemplary databases 507and 515 may be any type of database, including a database managed by adatabase management system (DBMS). In some embodiments, an exemplaryDBMS-managed database may be specifically programmed as an engine thatcontrols organization, storage, management, and/or retrieval of data inthe respective database. In some embodiments, the exemplary DBMS-manageddatabase may be specifically programmed to provide the ability to query,backup and replicate, enforce rules, provide security, compute, performchange and access logging, and/or automate optimization. In someembodiments, the exemplary DBMS-managed database may be chosen fromOracle database, IBM DB2, Adaptive Server Enterprise, FileMaker,Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQLimplementation. In some embodiments, the exemplary DBMS-managed databasemay be specifically programmed to define each respective schema of eachdatabase in the exemplary DBMS, according to a particular database modelof the present disclosure which may include a hierarchical model,network model, relational model, object model, or some other suitableorganization that may result in one or more applicable data structuresthat may include fields, records, files, and/or objects. In someembodiments, the exemplary DBMS-managed database may be specificallyprogrammed to include metadata about the data that is stored.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be specifically configured to operate in an cloudcomputing/architecture such as, but not limiting to: infrastructure aservice (IaaS), platform as a service (PaaS), and/or software as aservice (SaaS). FIGS. 17 and 18 illustrate schematics of exemplaryimplementations of the cloud computing/architecture(s) in which theexemplary inventive computer-based systems/platforms, the exemplaryinventive computer-based devices, and/or the exemplary inventivecomputer-based components of the present disclosure may be specificallyconfigured to operate.

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to utilize one or more exemplary AI/machinelearning techniques chosen from, but not limited to, decision trees,boosting, support-vector machines, neural networks, nearest neighboralgorithms, Naive Bayes, bagging, random forests, and the like. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an exemplary neutral network technique may be one of,without limitation, feedforward neural network, radial basis functionnetwork, recurrent neural network, convolutional network (e.g., U-net)or other suitable network. In some embodiments and, optionally, incombination of any embodiment described above or below, an exemplaryimplementation of Neural Network may be executed as follows:

i) Define Neural Network architecture/model,ii) Transfer the input data to the exemplary neural network model,iii) Train the exemplary model incrementally,iv) determine the accuracy for a specific number of timesteps,v) apply the exemplary trained model to process the newly-received inputdata,vi) optionally and in parallel, continue to train the exemplary trainedmodel with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, the exemplary trained neural network model mayspecify a neural network by at least a neural network topology, a seriesof activation functions, and connection weights. For example, thetopology of a neural network may include a configuration of nodes of theneural network and connections between such nodes. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the exemplary trained neural network model may also be specifiedto include other parameters, including but not limited to, biasvalues/functions and/or aggregation functions. For example, anactivation function of a node may be a step function, sine function,continuous or piecewise linear function, sigmoid function, hyperbolictangent function, or other type of mathematical function that representsa threshold at which the node is activated. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary aggregation function may be a mathematical function thatcombines (e.g., sum, product, etc.) input signals to the node. In someembodiments and, optionally, in combination of any embodiment describedabove or below, an output of the exemplary aggregation function may beused as input to the exemplary activation function. In some embodimentsand, optionally, in combination of any embodiment described above orbelow, the bias may be a constant value or function that may be used bythe aggregation function and/or the activation function to make the nodemore or less likely to be activated.

At least some aspects of the present disclosure will now be describedwith reference to the following numbered clauses.

1. A method may include:

receiving, by a logic layer processor, over a communication network,from a plurality of electronic resources, initial user personalidentifiable information (PII) of a user of a plurality of users;

where user PII may include a plurality of data elements;

classifying, by the logic layer processor, the plurality of dataelements of the initial PII of the user to populate a profile map datastructure having a standardized predefined data schema of a plurality ofvector elements so as to form a user-specific profile map data structureof the user, that may include at least a plurality of:

(i) a demographic user-specific parameter,

(ii) a psychographic user-specific parameter,

(iii) a behavioral user-specific parameter

(iv) a quantitative user-specific parameter, or

(v) any combination thereof;

iteratively receiving, by the logic layer processor, over thecommunication network, from the plurality of electronic resources,additional user personal identifiable information (PII) of the userbased at least in part on the user-specific profile map data structure;

iteratively classifying, by the logic layer processor, the additionaluser PII of the user to update the user-specific profile map datastructure of the user; and

enabling, by the logic layer processor, a plurality of user-specificdata management software functions based on the user-specific profilemap data structure.

2. The method according to clause 1, where at least one user-specificdata management software function from the plurality of user-specificdata management software functions may be configured to generate auser-specific profile interface for display on a computing device of theuser based on the updated user-specific profile map data structure ofthe user; and

where the user-specific profile interface may include a plurality ofgraphical elements that may be configured to:

(i) allow the user to adjust the user-specific profile map datastructure of the user,(ii) grant an access right to a person, an entity, or both, to access aportion of the user-specific profile map data structure of the user, or(iii) any combination thereof.3. The method as in any of clauses 1-2, where at least one user-specificdata management software function from the plurality of user-specificdata management software functions may be configured to anonymize theupdated user-specific profile map data structure of the user to generatea synthetic profile map of the user; and

where the synthetic profile map cannot be used to identify or infer anyuser identifying data of the user.

4. The method as in any of clauses 1-4, may further include:

receiving, by the logic layer processor, over the communication networkthrough an application programming interface (API) from a remotecomputing device of a person, an entity, or both, a request for anaccess right to a portion of the user-specific profile map datastructure of the user.

5. The method according to clause 4, where at least one user-specificdata management software function from the plurality of user-specificdata management software functions may be configured to grant the accessright;

and may further include:

extracting, by the logic layer processor, from the updated user-specificprofile map data structure, the requested portion of the user-specificprofile map data structure of the user; and

returning, by the logic layer processor, through the API, to the remotecomputing device over the communication network, the requested portionof the user-specific profile map data structure of the user.

6. The method according to clause 5, where the requested portion of theuser-specific profile map data structure of the user may be configuredto automatically populate at least one pre-defined software objectdesignated by the person, the entity, or both.7. The method as in any of clauses 5-6, where at least one seconduser-specific data management software function from the plurality ofuser-specific data management software functions may be configured toanonymize the requested portion of the user-specific profile map datastructure to generate a synthetic PII of the user;

where the synthetic PII of the user cannot be used to identify or inferany user identifying data of the user; and

may further include returning, by the logic layer processor, through theAPI, to the remote computing device over the communication network, thesynthetic PII of the user.

8. The method as in any of clauses 1-4, 6 or 7 may further include:

validating, by the logic layer processor, at least one data element ofthe user PII based at least in part on at least one electronic resource.

9. The method according to clause 8, where the at least one electronicresource may be a user-independent electronic resource.10. The method according to clause 9 may further include:

accessing, by the logic layer processor, over the communication network,the user-independent electronic resource to validate the at least onedata element of the user PII.

11. The method as in any of clauses 1-4, 6-8, or 10, where the pluralityof data elements may include at least one form and metadata of the atleast one form.12. The method according to clause 11, where the at least one form maybe at least one of: a financial document, a bank statement, a creditcard statement, an appraisal document, a home inspection document, asigned contract, a disclosure form, a driver license, a tax return, aW-2 form, a 1099 form, a pay stub, a financial-related document, or anycombination thereof13. The method as in any of clauses 1-4, 6-8, or 10-12, whereiteratively classifying the additional user PII of the user may includeiteratively classifying the additional user PII of the user until theuser-specific profile map data structure of the user is populated withdata that exceeds a predefined data threshold.14. The method according to clause 13, where the predefined datathreshold may be based on demographic factors, psychographic factors,behavioral factors, user interface (UI) design psychology, or anycombination thereof.15. A system may include:

a non-transitory memory; and

at least one logic layer processor may be configured to execute computercode stored in the non-transitory memory that causes the at least onelogic layer processor to:

receive over a communication network, from a plurality of electronicresources, initial user personal identifiable information (PII) of auser of a plurality of users;

-   -   where user PII may include a plurality of data elements;

classify the plurality of data elements of the initial PII of the userto populate a profile map data structure having a standardizedpredefined data schema of a plurality of vector elements so as to form auser-specific profile map data structure of the user, that may includeat least a plurality of:

(i) a demographic user-specific parameter,

(ii) a psychographic user-specific parameter,

(iii) a behavioral user-specific parameter

(iv) a quantitative user-specific parameter, or

(v) any combination thereof;

iteratively receive over the communication network, from the pluralityof electronic resources, additional user personal identifiableinformation (PII) of the user based at least in part on theuser-specific profile map data structure;

iteratively classify the additional user PII of the user to update theuser-specific profile map data structure of the user; and

enable a plurality of user-specific data management software functionsbased on the user-specific profile map data structure.

16. The system according to clause 15, where at least one user-specificdata management software function from the plurality of user-specificdata management software functions may be configured to generate auser-specific profile interface for display on a computing device of theuser based on the updated user-specific profile map data structure ofthe user; and

where the user-specific profile interface may include a plurality ofgraphical elements that may be configured to:

-   -   (i) allow the user to adjust the user-specific profile map data        structure of the user,    -   (ii) grant an access right to a person, an entity, or both, to        access a portion of the user-specific profile map data structure        of the user, or    -   (iii) any combination thereof.        17. The system as in any of clauses 15-16, where at least one        user-specific data management software function from the        plurality of user-specific data management software functions        may be configured to anonymize the updated user-specific profile        map data structure of the user to generate a synthetic profile        map of the user; and

where the synthetic profile map cannot be used to identify or infer anyuser identifying data of the user.

18. The system as in any of clauses 15-17, where the at least one logiclayer processor may further be configured to:

receive over the communication network through an applicationprogramming interface (API) from a remote computing device of a person,an entity, or both, a request for an access right to a portion of theuser-specific profile map data structure of the user.

19. The system according to clause 18, where at least one user-specificdata management software function from the plurality of user-specificdata management software functions may be configured to grant the accessright;

and where the at least one logic layer processor may be furtherconfigured to:

extract from the updated user-specific profile map data structure, therequested portion of the user-specific profile map data structure of theuser; and

return through the API, to the remote computing device over thecommunication network, the requested portion of the user-specificprofile map data structure of the user.

20. The system according to clause 19, where the requested portion ofthe user-specific profile map data structure of the user may beconfigured to automatically populate at least one pre-defined softwareobject designated by the person, the entity, or both.21. The system as in any of clauses 19-20, where at least one seconduser-specific data management software function from the plurality ofuser-specific data management software functions may be configured toanonymize the requested portion of the user-specific profile map datastructure to generate a synthetic PII of the user;

where the synthetic PII of the user cannot be used to identify or inferany user identifying data of the user; and

wherein the at least one logic layer processor may be further configuredto return, through the API, to the remote computing device over thecommunication network, the synthetic PII of the user.

22. The system as in any of clauses 15-18, 20 or 21, where the at leastone logic layer processor may be further configured to validate at leastone data element of the user PII based at least in part on at least oneelectronic resource.23. The system according to clause 22, where the at least one electronicresource may be a user-independent electronic resource.24. The system according to clause 23, where the at least one logiclayer processor may further be configured to access over thecommunication network, the user-independent electronic resource tovalidate the at least one data element of the user PII.25. The system as in any of clauses 15-18, 20-22, or 24, where theplurality of data elements may include at least one form and metadata ofthe at least one form.26. The system according to clause 25, wherein the at least one form maybe at least one of: a financial document, a bank statement, a creditcard statement, an appraisal document, a home inspection document, asigned contract, a disclosure form, a driver license, a tax return, aW-2 form, a 1099 form, a pay stub, a financial-related document, or anycombination thereof.27. The system as in any of clauses 15-18, 20-22, or 24-26, where the atleast one logic layer processor may be configured to iterativelyclassify the additional user PII of the user by iteratively classifyingthe additional user PII of the user until the user-specific profile mapdata structure of the user is populated with data that exceeds apredefined data threshold.28. The system according to clause 27, where the predefined datathreshold may be based on demographic factors, psychographic factors,behavioral factors, user interface (UI) design psychology, or anycombination thereof.29. The system may include:

a non-transitory memory; and

at least one logic layer processor may be configured to execute computercode stored in the non-transitory memory that causes the at least onelogic layer processor to:

receive over a communication network, from a plurality of electronicresources, initial user personal identifiable information (PII) of auser of a plurality of users;

-   -   where user PII may include a plurality of data elements;

classify the plurality of data elements of the initial PII of the userto populate a profile map data structure so as to form a user-specificprofile map data structure of the user;

iteratively receive over the communication network, from the pluralityof electronic resources, additional user personal identifiableinformation (PII) of the user based at least in part on theuser-specific profile map data structure;

iteratively classify the additional user PII of the user to update theuser-specific profile map data structure of the user.

30. The method may include:

receiving, by a logic layer processor, over a communication network,from a plurality of electronic resources, initial user personalidentifiable information (PII) of a user of a plurality of users;

where user PII may include a plurality of data elements;

classifying, by the logic layer processor, the plurality of dataelements of the initial PII of the user to populate a profile map datastructure so as to form a user-specific profile map data structure ofthe user;

iteratively receiving, by the logic layer processor, over thecommunication network, from the plurality of electronic resources,additional user personal identifiable information (PII) of the userbased at least in part on the user-specific profile map data structure;

iteratively classifying, by the logic layer processor, the additionaluser PII of the user to update the user-specific profile map datastructure of the user.

31. A method may include:

receiving, by a logic layer processor, over a communication network,from a plurality of electronic resources, initial user personalidentifiable information (PII) of a user of a plurality of users;

-   -   where user PII may include a plurality of data elements;

classifying, by the logic layer processor, the plurality of dataelements of the initial PII of the user to populate a profile map datastructure so as to form a user-specific profile map data structure ofthe user;

iteratively receiving, by the logic layer processor, over thecommunication network, from the plurality of electronic resources,additional user personal identifiable information (PII) of the userbased at least in part on the user-specific profile map data structure;

iteratively classifying, by the logic layer processor, the additionaluser PII of the user to update the user-specific profile map datastructure of the user;

enabling, by the logic layer processor, a plurality of user-specificdata management software functions based on the user-specific profilemap data structure.

32. A system may include:

a non-transitory memory; and

at least one logic layer processor configured to execute computer codestored in the non-transitory memory that causes the at least one logiclayer processor to:

receive over a communication network, from a plurality of electronicresources, initial user personal identifiable information (PII) of auser of a plurality of users;

-   -   where user PII may include a plurality of data elements;

classify the plurality of data elements of the initial PII of the userto populate a profile map data structure so as to form a user-specificprofile map data structure of the user;

iteratively receive over the communication network, from the pluralityof electronic resources, additional user personal identifiableinformation (PII) of the user based at least in part on theuser-specific profile map data structure;

iteratively classify the additional user PII of the user to update theuser-specific profile map data structure of the user;

enable a plurality of user-specific data management software functionsbased on the user-specific profile map data structure.

33. A method may include:

receiving, by a logic layer processor, over a communication network,from a plurality of electronic resources, initial user personalidentifiable information (PII) of a user of a plurality of users;

-   -   where user PII may include a plurality of data elements;

classifying, by the logic layer processor, the plurality of dataelements of the initial PII of the user to populate a profile map datastructure having a standardized predefined data schema of a plurality ofvector elements so as to form a user-specific profile map data structureof the user;

iteratively receiving, by the logic layer processor, over thecommunication network, from the plurality of electronic resources,additional user personal identifiable information (PII) of the userbased at least in part on the user-specific profile map data structure;

iteratively classifying, by the logic layer processor, the additionaluser PII of the user to update the user-specific profile map datastructure of the user.

34. A system may include:

a non-transitory memory; and

at least one logic layer processor configured to execute computer codestored in the non-transitory memory that causes the at least one logiclayer processor to:

receive over a communication network, from a plurality of electronicresources, initial user personal identifiable information (PII) of auser of a plurality of users;

-   -   where user PII may include a plurality of data elements;

classify the plurality of data elements of the initial PII of the userto populate a profile map data structure having a standardizedpredefined data schema of a plurality of vector elements so as to form auser-specific profile map data structure of the user;

iteratively receive over the communication network, from the pluralityof electronic resources, additional user personal identifiableinformation (PII) of the user based at least in part on theuser-specific profile map data structure;

iteratively classify the additional user PII of the user to update theuser-specific profile map data structure of the user.

Publications cited throughout this document are hereby incorporated byreference in their entirety. While one or more embodiments of thepresent disclosure have been described, it is understood that theseembodiments are illustrative only, and not restrictive, and that manymodifications may become apparent to those of ordinary skill in the art,including that various embodiments of the inventive methodologies, theinventive systems/platforms, and the inventive devices described hereincan be utilized in any combination with each other. Further still, thevarious steps may be carried out in any desired order (and any desiredsteps may be added and/or any desired steps may be eliminated).

1. A method, comprising: receiving, by a logic layer processor, over acommunication network, from a plurality of electronic resources, initialuser personal identifiable information (PII) of a user of a plurality ofusers; wherein user PII comprises a plurality of data elements;classifying, by the logic layer processor, the plurality of dataelements of the initial PII of the user to populate a profile map datastructure having a standardized predefined data schema of a plurality ofvector elements so as to form a user-specific profile map data structureof the user, comprising at least a plurality of: (i) a demographicuser-specific parameter, (ii) a psychographic user-specific parameter,(iii) a behavioral user-specific parameter (iv) a quantitativeuser-specific parameter, or (v) any combination thereof; iterativelyreceiving, by the logic layer processor, over the communication network,from the plurality of electronic resources, additional user personalidentifiable information (PII) of the user based at least in part on theuser-specific profile map data structure; iteratively classifying, bythe logic layer processor, the additional user PII of the user to updatethe user-specific profile map data structure of the user; and enabling,by the logic layer processor, a plurality of user-specific datamanagement software functions based on the user-specific profile mapdata structure.
 2. The method according to claim 1, wherein at least oneuser-specific data management software function from the plurality ofuser-specific data management software functions is configured togenerate a user-specific profile interface for display on a computingdevice of the user based on the updated user-specific profile map datastructure of the user; and wherein the user-specific profile interfacecomprises a plurality of graphical elements that are configured to: (i)allow the user to adjust the user-specific profile map data structure ofthe user, (ii) grant an access right to a person, an entity, or both, toaccess a portion of the user-specific profile map data structure of theuser, or (iii) any combination thereof.
 3. The method according to claim1, wherein at least one user-specific data management software functionfrom the plurality of user-specific data management software functionsis configured to anonymize the updated user-specific profile map datastructure of the user to generate a synthetic profile map of the user;and wherein the synthetic profile map cannot be used to identify orinfer any user identifying data of the user.
 4. The method according toclaim 1, further comprising: receiving, by the logic layer processor,over the communication network through an application programminginterface (API) from a remote computing device of a person, an entity,or both, a request for an access right to a portion of the user-specificprofile map data structure of the user.
 5. The method according to claim4, wherein at least one user-specific data management software functionfrom the plurality of user-specific data management software functionsis configured to grant the access right; and further comprising:extracting, by the logic layer processor, from the updated user-specificprofile map data structure, the requested portion of the user-specificprofile map data structure of the user; and returning, by the logiclayer processor, through the API, to the remote computing device overthe communication network, the requested portion of the user-specificprofile map data structure of the user.
 6. The method according to claim5, wherein the requested portion of the user-specific profile map datastructure of the user is configured to automatically populate at leastone pre-defined software object designated by the person, the entity, orboth.
 7. The method according to claim 5, wherein at least one seconduser-specific data management software function from the plurality ofuser-specific data management software functions is configured toanonymize the requested portion of the user-specific profile map datastructure to generate a synthetic PII of the user; wherein the syntheticPII of the user cannot be used to identify or infer any user identifyingdata of the user; and further comprising returning, by the logic layerprocessor, through the API, to the remote computing device over thecommunication network, the synthetic PII of the user.
 8. The methodaccording to claim 1, further comprising: validating, by the logic layerprocessor, at least one data element of the user PII based at least inpart on at least one electronic resource.
 9. The method according toclaim 8, wherein the at least one electronic resource is auser-independent electronic resource.
 10. The method according to claim9, further comprising: accessing, by the logic layer processor, over thecommunication network, the user-independent electronic resource tovalidate the at least one data element of the user PII.
 11. The methodaccording to claim 1, wherein the plurality of data elements comprisesat least one form and metadata of the at least one form.
 12. The methodaccording to claim 11, wherein the at least one form is at least one of:a financial document, a bank statement, a credit card statement, anappraisal document, a home inspection document, a signed contract, adisclosure form, a driver license, a tax return, a W-2 form, a 1099form, a pay stub, a financial-related document, or any combinationthereof.
 13. The method according to claim 1, wherein iterativelyclassifying the additional user PII of the user comprises iterativelyclassifying the additional user PII of the user until the user-specificprofile map data structure of the user is populated with data thatexceeds a predefined data threshold.
 14. The method according to claim13, wherein the predefined data threshold is based on demographicfactors, psychographic factors, behavioral factors, user interface (UI)design psychology, or any combination thereof.
 15. A system, comprising:a non-transitory memory; and at least one logic layer processorconfigured to execute computer code stored in the non-transitory memorythat causes the at least one logic layer processor to: receive over acommunication network, from a plurality of electronic resources, initialuser personal identifiable information (PII) of a user of a plurality ofusers; wherein user PII comprises a plurality of data elements; classifythe plurality of data elements of the initial PII of the user topopulate a profile map data structure having a standardized predefineddata schema of a plurality of vector elements so as to form auser-specific profile map data structure of the user, comprising atleast a plurality of: (i) a demographic user-specific parameter, (ii) apsychographic user-specific parameter, (iii) a behavioral user-specificparameter (iv) a quantitative user-specific parameter, or (v) anycombination thereof; iteratively receive over the communication network,from the plurality of electronic resources, additional user personalidentifiable information (PII) of the user based at least in part on theuser-specific profile map data structure; iteratively classify theadditional user PII of the user to update the user-specific profile mapdata structure of the user; and enable a plurality of user-specific datamanagement software functions based on the user-specific profile mapdata structure.
 16. The system according to claim 15, wherein at leastone user-specific data management software function from the pluralityof user-specific data management software functions is configured togenerate a user-specific profile interface for display on a computingdevice of the user based on the updated user-specific profile map datastructure of the user; and wherein the user-specific profile interfacecomprises a plurality of graphical elements that are configured to: (i)allow the user to adjust the user-specific profile map data structure ofthe user, (ii) grant an access right to a person, an entity, or both, toaccess a portion of the user-specific profile map data structure of theuser, or (iii) any combination thereof.
 17. The system according toclaim 15, wherein at least one user-specific data management softwarefunction from the plurality of user-specific data management softwarefunctions is configured to anonymize the updated user-specific profilemap data structure of the user to generate a synthetic profile map ofthe user; and wherein the synthetic profile map cannot be used toidentify or infer any user identifying data of the user.
 18. The systemaccording to claim 15, wherein the at least one logic layer processor isfurther configured to: receive over the communication network through anapplication programming interface (API) from a remote computing deviceof a person, an entity, or both, a request for an access right to aportion of the user-specific profile map data structure of the user. 19.The system according to claim 18, wherein at least one user-specificdata management software function from the plurality of user-specificdata management software functions is configured to grant the accessright; and wherein the at least one logic layer processor is furtherconfigured to: extract from the updated user-specific profile map datastructure, the requested portion of the user-specific profile map datastructure of the user; and return through the API, to the remotecomputing device over the communication network, the requested portionof the user-specific profile map data structure of the user.
 20. Thesystem according to claim 19, wherein the requested portion of theuser-specific profile map data structure of the user is configured toautomatically populate at least one pre-defined software objectdesignated by the person, the entity, or both.
 21. The system accordingto claim 19, wherein at least one second user-specific data managementsoftware function from the plurality of user-specific data managementsoftware functions is configured to anonymize the requested portion ofthe user-specific profile map data structure to generate a synthetic PIIof the user; wherein the synthetic PII of the user cannot be used toidentify or infer any user identifying data of the user; and wherein theat least one logic layer processor is further configured to return,through the API, to the remote computing device over the communicationnetwork, the synthetic PII of the user.
 22. The system according toclaim 15, wherein the at least one logic layer processor is furtherconfigured to validate at least one data element of the user PII basedat least in part on at least one electronic resource.
 23. The systemaccording to claim 22, wherein the at least one electronic resource is auser-independent electronic resource.
 24. The system according to claim23, wherein the at least one logic layer processor is further configuredto access over the communication network, the user-independentelectronic resource to validate the at least one data element of theuser PII.
 25. The system according to claim 15, wherein the plurality ofdata elements comprises at least one form and metadata of the at leastone form.
 26. The system according to claim 25, wherein the at least oneform is at least one of: a financial document, a bank statement, acredit card statement, an appraisal document, a home inspectiondocument, a signed contract, a disclosure form, a driver license, a taxreturn, a W-2 form, a 1099 form, a pay stub, a financial-relateddocument, or any combination thereof.
 27. The system according to claim15, wherein the at least one logic layer processor is configured toiteratively classify the additional user PII of the user by iterativelyclassifying the additional user PII of the user until the user-specificprofile map data structure of the user is populated with data thatexceeds a predefined data threshold.
 28. The system according to claim27, wherein the predefined data threshold is based on demographicfactors, psychographic factors, behavioral factors, user interface (UI)design psychology, or any combination thereof.